Contextual Adaptive Knowledge Visualization Environments

ISSN 1479-4411 01 ©Academic Publishing International Ltd Reference this paper as: Bai, X, White, D and Sundaram, D. ―Contextual Adaptive Knowledge Visualization Environments‖ The

Electronic Journal of Knowledge Management Volume 10 Issue 1 (pp01-14, available online

at www.ejkm.com

Contextual Adaptive Knowledge Visualization Environments

Xiaoyan Bai, David White and David Sundaram Department of Information Systems and Operations Management, University of Auckland, New Zealand [email protected] [email protected] [email protected] Abstract: As an essential component of knowledge management systems, visualizations assist in creating, transferring and sharing knowledge in a wide range of contexts where knowledge workers need to explore, manage and get insights from tremendous volumes of data. Knowledge visualization context may incorporate any information in regard to the decisional problem context within which visualizations are applied, the visualization profiles of knowledge workers as well as their intended purposes. Due to the inherent dynamic nature, these contextual factors may cause the changing visualization requirements and difficulties in maintaining the effectiveness of a knowledge visualization when contextual changes occur. To address the contextual complexities, visualization systems to support knowledge management need to provide flexible support for the creation, manipulation, transformation and improvement of visualization solutions. Furthermore, they should be able to sense, analyze and respond to the contextual changes so as to support in maintaining the effectiveness of the solutions. In addition, they need to possess the capability to mediate between the problem and the knowledge workers through provision of action and presentation languages. However, many visualization systems tend to provide weak support for fulfilling these system requirements. They do not provide adequate flexibility for adapting the visualizations to fit different knowledge visualization contexts. This motivated us to propose and implement a flexible knowledge visualization system for better aiding knowledge creation, transfer and sharing, namely, Contextual Adaptive Visualization Environment (CAVE). CAVE provides flexible support for (1) sensing and being aware of changes in the problem, purpose and/or knowledge worker contexts, (2) interpreting the changes through relevant analysis and (3) responding to the changes through appropriate re-design and re-modelling of visual compositions to address the problem. In order to fulfil the requirements posed above, we developed and proposed conceptual models and frameworks which are further elucidated through system-oriented architectures and implementations. Keywords: knowledge visualization, knowledge visualization context, knowledge creation and sharing, CAVE model, CAVE framework, and CAVE implementation

1. Introduction

Knowledge visualization is concerned with designing, implementing and applying appropriate visual representations to create, transform and communicate knowledge. Knowledge visualization is playing an increasingly important role in knowledge management systems (Burkhard, 2004; Cañas et al., 2005; Pinaud et al., 2006; Eppler and Burkhard, 2007; Bresciani and Eppler, 2009; Bresciani and Eppler, 2010; Eppler and Burkhard, 2011). Knowledge visualizations can be designed and developed by leveraging extensive visualization techniques and systems in the field of information visualization. The existing visualization techniques have been reviewed and categorized by researchers and practitioners according to their features such as data types that visualizations support, purposes that visualizations fulfil, and problem domains where visualizations are applied (Card, Mackinlay and Shneiderman, 1999; Chi, 2000; Chen, 2006; Spence, 2007; Heer, Bostock and Ogievetsky, 2010). Visualizations can be applied to a wide range of contexts where people need to explore, create, represent, present, transfer and/or share knowledge. In general, knowledge visualization context incorporates the decisional problem context where knowledge visualizations are deployed, the visualization profiles of knowledge workers as well as their intended purposes to be achieved via applying the visualizations. More specifically, the decisional problem context may involve relevant problem situations, physical surroundings, time, knowledge visualization tasks and requirements, and social and technological contexts. The knowledge worker context may cover the knowledge workers’ cognitive styles, personal preferences, prior knowledge of relevant problem domain(s), skill acquisition abilities, age, gender, etc. The purpose context describes the various and sometimes even conflicting goals and objectives that the knowledge workers attempt to achieve through applying the visualizations.

mailto:[email protected]



Electronic Journal of Knowledge Management Volume 10 Issue 1 2012

www.ejkm.com 2 ©Academic Publishing International Ltd

These contextual factors are diverse and dynamic, which, in turn, may cause huge complexity inherent in knowledge visualization context. As a result of this, the visualization requirements for solving the same decisional problem may vary when contextual changes occur. The same knowledge visualizations that are appropriate under particular problem and knowledge worker contexts might not even be relevant when certain contextual changes take place. For instance, knowledge workers of the same knowledge visualization may vary over time. Different knowledge workers may have different visualization preferences such as color, shape and interaction styles. Even for the same knowledge worker, the visualization requirements may change when the knowledge worker becomes more familiar with the relevant problem domain and the visualization system in use. A beginner-level knowledge worker often needs step-by-step support for how to manipulate visualizations while an expert-level knowledge worker may need more support for customizing visualization to complete sophisticated tasks. Knowledge visualization context is complex and dynamic in nature, which may cause two major problems with developing effective knowledge visualizations. Firstly, many visualization systems to support knowledge management often have little concern on knowledge visualization context. Context complexity can significantly affect the effectiveness of a knowledge visualization in terms of how well it can support a knowledge worker to solve the decisional problem of interest and achieve the intended purpose. The lack of concerns on such impact may incur issues with ineffective knowledge visualization design and even visualization misuse. Secondly, there is a lack of support for developing and/or adapting knowledge visualizations to address the changing requirements caused by visualization context complexity. Though a knowledge visualization could be designed for a particular context, it can very soon get out of sync with respect to the context. Maintaining visualization effectiveness across contexts is a big challenge. To address the above context-related problems, visualization systems to support knowledge management need to provide flexible support for creating, manipulating, transforming, improving and disposing visualization solutions. Meanwhile, they should support knowledge workers to flexibly adapt visualizations to address context dynamics and maintain the visualization effectiveness. However, many existing knowledge management systems and their visualizations tend to provide weak support for these requirements. The above problems, issues and requirements associated with knowledge visualization context motivated us to propose and implement a flexible system for better aiding knowledge creation, transfer and sharing, namely, Contextual Adaptive Visualization Environment (CAVE). As illustrated in Figure 1, CAVE is a context-sensitive, adaptive platform that can provide flexible support for continuously sensing the dynamic problem, purpose and knowledge worker contexts. It assists knowledge workers to define the contextual changes through proper analysis and identify the associated visualization requirement changes. Also, CAVE helps the knowledge workers to respond to the changes and requirements through appropriate re-design and re-modelling of visual compositions to address the problem of interest. In this paper, we introduce a framework of knowledge visualization context in section 2. We then proceed to explicate the definition of CAVE and its high-level functional requirements in section 3. Next, in section 4 we propose a conceptual model to deepen the understanding of CAVE definition and how it can address contextual complexities and the subsequent changing requirements. After this, a framework is proffered to guide the design and development of CAVE in section 5. In order to prove the validity of our proposed concepts, models and framework, we implemented a prototypical system to demonstrate how CAVE can adapt to both macro-level and micro-level contextual changes in section 6.

2. Knowledge visualization contexts

To illustrate and understand the complexity of context, many researchers have attempted to articulate and categorize contextual information, such as Dey (2001), Schmidt et al. (2000), Chen and Kotz (2000), Schilit, Adams and Want (1994), and Wu and Chen (2009). For instance, Schilit, Adams and Want (1994) identify three general contextual groups, i.e. computing context, user context and physical context. This classification scheme is further extended by Chen and Kotz (2000) with adding in two new groups: time context and context history. Building on top of these general context classifications and domain related context categorizations in mobile computing and adaptive geographical information systems (e.g. Petit, Ray and Claramunt (2006), and Nivala and Sarjakoski

Xiaoyan Bai et al.

www.ejkm.com 3 ISSN 1479-4411

(2003)), Wu and Chen (2009) proposed four contextual groups. They are context, activity context (i.e. task, tool and data), physical context (including location, orientation, physical surroundings, time, and movement state), and system context (i.e. system style and capability).

Figure 1: A high-level sense and response model of CAVE

In the domain of visualization, knowledge visualization context involves the information of any environmental entities that influence knowledge visualization design, development, application and evaluation. By reviewing and synthesizing the extant contextual classifications as well as the literature about visualization contextual information (e.g. Shneiderman (1996), Dreyfus and Dreyfus (1986), IBM Many Eyes (2011), Card, Mackinlay and Shneiderman (1999), Eppler and Burkhard (2007), Lee, Lee and Lee (2009), Stanford (2001), Donald et al. (2009)), we propose a Knowledge Visualization Context Framework (Figure 2). As illustrated in Figure 2, we classify knowledge visualization context into three fundamental dimensions, that is, the decisional problem context within which visualizations are deployed, the situational context of knowledge workers, and the purpose(s) which the knowledge workers attempt to achieve via applying the visualizations. Each dimension consists of a set of contextual categories. There are four common contextual categories that are shared among these dimensions, i.e. knowledge generation, knowledge representation, knowledge presentation, and time. Detailed information about these contextual dimensions and their potential impact on knowledge visualization design and implementation are presented in sub-sections 2.1-2.4.

2.1 Problem context

This problem dimension is concerned with the contextual information with regard to the problem situation to be supported and potential solutions. A brief summary of typical contextual factors involved in problem dimension and categories is provided in Table 1.



Figure 2: Knowledge visualization context framework

Table 1: Problem context

Contextual Dimension

Contextual Categories Description & Example

Problem Context

Problem Situation

E.g. Statistical and categorical data management, digital library management, personal services support, complex documents

management, history management, classifications management, networks management, etc.

Knowledge Types Declarative knowledge, procedural knowledge, experiential knowledge, people-related knowledge, location-based knowledge, scenario-based

knowledge, and normative/value-based knowledge Knowledge

Management Tasks Knowledge creation, codification, transfer, identification,

application/learning, measurement/assessment, and signaling Visualization Tasks Overview, zoom, filter, details-on-demand, relate, history, and extract

Location E.g. latitude, longitude, altitude, city, suburb, country, etc.

Physical Surroundings Lighting, temperature, surrounding landscape, weather conditions,

noise levels, etc.

Movement State E.g. Speed

Knowledge Generation

Data transformation requirements of a decisional problem

Knowledge Representation

Data type, data quality, data volume, and relevant techniques (e.g. structured text/tables, mental images/stories, heuristic sketch,

conceptual diagram, image/visual metaphor, knowledge map, etc.)

Knowledge Presentation

Semantic layer, animation, interaction, output device (size, resolution), input device (touch panel, keyboard, mouse, etc.), network

connectivity, and communication costs/bandwidth

Time Time-series data involved in a decisional problem, when the

effectiveness of a visualization solution is confirmed, etc.

Xiaoyan Bai et al.


Knowledge visualizations nowadays may be employed in many problem domains and/or disciplines to support diverse user purposes and tasks involved in information/knowledge navigation, retrieval, query, discovery and/or interpretation. For example, Card et al. (1999) have identified seven representative domains, namely, statistical and categorical data management, digital library management, personal services support, complex documents management, history management, classifications management, and networks management. Quite often, real-world decisional problems span multiple application domains, instead of merely residing within a single domain. For example, in a large utility (e.g. electricity and gas) infrastructure company, the senior management may be interested in exploring and visualizing the patterns and/or trends embedded in the problematic gas and electricity connections (on maps) which have incurred exceptionally high maintenance costs. This issue covers three typical application domains, that is, statistical and categorical data management, complex documents management, and networks management. More specifically, the application domain of statistical and categorical data management is involved due to the need of visualizing accounting data (i.e. maintenance costs of electricity connections and gas pipelines). Complex documents management is required to handle the reports of electricity connection and gas pipeline faults. Networks management is a necessity for effectively generating map-based electricity and gas networks with problematic connections highlighted.

2.2 Knowledge worker context

The knowledge worker dimension incorporates any stakeholder related aspects that can affect the design, development, cognition, interpretation and/or evaluation of a visualization by different types of stakeholders. Representative contextual factors relating to this dimension are summarized in Table 2.

Table 2: Knowledge worker context


Contextual Categories Description & Example

Knowledge Worker Context

Knowledge Worker Type

E.g. individual, team, community of practice, organization and the public

Knowledge Worker Profile

Cognitive styles, personal characteristics and preferences, educational background, culture and social background (faith, nationality, etc.), personality (introversive/extroversive), physical condition (disability, left/right hands, etc.), age, gender, mood, etc.

Knowledge Worker Ability

Prior knowledge (e.g. knowledge in the problem domain, past experience with manipulating the visualization, past experience with using the visualization system), skill acquisition ability (i.e. novice, advanced beginner, competent, proficient, expert, and master levels), etc.


Data transformation requirements of a knowledge worker


Data type, data quality, data volume, and relevant techniques


Semantic layer, animation, interaction, output device, input device, network connectivity, and communication costs/bandwidth

Time Time-series data associated with a knowledge worker, e.g. when a visualization solution is effective for the knowledge worker, etc.

Along the way of accomplishing various user tasks involved in the associated problem domains, knowledge workers may go through six principal stages of learning or skill development through which they progress to achieve higher levels of proficiency and expertise (Dreyfus & Dreyfus, 1986). These fundamental learning development stages are novice, advanced beginner, competent, proficient, expertise and master. Each of the above learning development stage is also associated with six mental functions, i.e. similarity recognition, aspect recognition, decision paradigm, perspective, commitment, and monitoring. These learning development stages and mental functions form the building blocks of the skill acquisition model proposed by Dreyfus and Dreyfus (1986). As going through the learning development stages from novice to master, knowledge workers gradually develop their abilities of resolving new problems through recognizing the similarities between the new problem situation and previous problem situations that they have experienced. This, in turn, enables them to gain stronger problem solving and decision making capabilities and better performance. Knowledge workers with different abilities at different learning development stages may have different sets of tasks to complete so as to address certain problem issues of interest and/or achieve certain purposes, which can lead to different requirements for visualizations.



More specifically, according to Dreyfus and Dreyfus (1986), people at beginner levels are only capable of perceiving and understanding simple clues in a problem context and recognizing very limited similar features to their experienced problems. They have to depend on the available relevant rules and directions for guiding their activities, and on deliberately monitoring their own performance and getting feedback. The lack of guidance on performing certain tasks or the lack of previous experiences for resolving relevant problems may cause them to present low performance. In contrast, people with higher levels of expertise often have stronger capabilities to understand and resolve problems though basing their judgments against past experiences and relevant knowledge, which often leads to a better performance (Dreyfus and Dreyfus, 1986). They are more likely to cope with complex problems and see through complicated situations, decide task requirements for resolving the problems, and perform the tasks with less monitoring efforts and more commitment to problem solving activities.

2.3 Purpose context

The purpose dimension contains contextual information about what a knowledge worker is trying to achieve through applying visualizations in a particular domain to address/accomplish certain problems/tasks. Table 3 outlines the typical contextual factors involved in the purpose context.

Table 3: Purpose context


Contextual Categories

Description & Example

Purpose Context

Domain Related Purpose

E.g. to support statistical data analysis, to manage digital libraries, to provide personal services support, to manage complex documents, to aid historical data management, to manage classifications, to visualize networks, etc.

Knowledge Worker Related Purpose

E.g. to support financial analysis of last year, to support education and E-learning in the University of Auckland, to support military debriefing, etc.)

Task Related Purpose

E.g. to discovery relationships/patterns from a large volume of data points, facilitate data comparison, track/display trends over time, illustrate structure or composition, analyze words/texts, and explore geographical data


Data transformation requirements for achieving certain purposes


Data type, data quality, data volume, and relevant techniques


Semantic layer, animation, interaction, output device, input device, network connectivity, and communication costs/bandwidth

Time Purpose related time data, e.g. when a purpose becomes relevant

The purpose context involves three essential perspectives, that is, application domain, knowledge, and task related purposes. The knowledge worker perspective specifies visualization purpose from the angle of what objectives knowledge workers attempt to achieve via the visualization within their specific context. The task perspective depicts the visualization purpose from the angle of what user tasks a knowledge visualization aims to support. The domain perspective defines the visualization purpose from the angle of what in general the visualization is trying to fulfil within its particular application fields/contexts. In addition, purpose context incorporates information and requirements of purpose related information generation/representation/presentation and time.

2.4 Contextual impact on knowledge visualization design and implementation

The changing and dynamic problem, purpose and knowledge worker contexts may lead to changing visualization requirements. For example, knowledge workers at beginner levels can normally deal with smaller chunks of data at one time and thus require visualization designs containing the support/guidance for basic operations to accomplish a particular task. Compared to them, knowledge workers with higher levels of expertise are often able to process relatively large chunks of data. They may not need visualizations to provide basic operation guidance but rather the support for more complicated tasks such as advanced information analysis. Furthermore, the problem, purpose and knowledge worker contexts may significantly influence the design and implementation of visualizations in knowledge management systems. For instance, knowledge visualization development is intimately coupled with mental tasks and attributes

Xiaoyan Bai et al.


associated with different learning development stages. Knowledge visualization design and implementation should concern to what extent the knowledge workers rely on clearly defined decision making rules or task instructions, how well they are aware of the underlying problem situations, how easily they can recognize similarities between the problem under investigation and the problems that they resolved in the past, how accurately they may identify and understand the relevant task requirements from the similarities, and how effectively they can monitor their own performance. In addition, the visualization system involved in knowledge management should offer adequate support for personalization and customization so as to better serve different knowledge workers. Knowledge management systems should also provide appropriate adaptability mechanisms to assist the knowledge workers with their transition from beginners through to masters/experts. To address the complexities involved in knowledge visualization context, we introduce contextual adaptive visualization environment in the following section.

3. Contextual Adaptive Visualization Environment (CAVE)

We define a Contextual Adaptive Visualization Environment as a context-sensitive, adaptive platform that helps knowledge workers to continuously monitor the changing/evolving context of their interested problem, sense and analyze the changes in the context, and respond to the problem by utilizing data, models (problem and visual), solvers and scenarios to create and manage effective visual compositions (Figure 3). The responses by the system and by the knowledge worker could be at different levels. It could be a parametric change (single loop learning), introduction/modification/deletion of variables of model (double loop learning), and/or transformational changes at a deep and broad level (triple loop learning). The key purpose of CAVE is to sense, analyze and respond to the changes in the visualization contexts. Furthermore, CAVE mediates between the problem and the knowledge workers through the explicit provision of action and presentation languages. To address the contextual complexities, CAVE provides flexible support for (1) creating/manipulating/transforming/improving/disposing visualization solutions and (2) maintaining the effectiveness of the solutions within the changing/evolving problem context. This definition of CAVE raises many requirements and features which are elucidated in the following sub-sections 3.1-3.4.

Figure 3: Contextual adaptive visualization environment model



3.1 Visualization creation

To ensure that visualizations can match the problem, purpose and knowledge worker contexts, new visualizations are often required to support various tasks. Accordingly, knowledge visualization systems need to enable a knowledge worker to build new visualizations in a flexible fashion. The knowledge worker should be able to develop new visualizations either from scratch or based on existing reusable visualization components. As demonstrated in Chi and Riedl’s (1998) data state model, this requirement can be achieved by selecting and integrating appropriate within-stage and between-stage operations. Systems fulfilling this requirement may significantly enhance the knowledge worker’s capability of handling the changing visualization purposes and contexts.

3.2 Visualization modification/customization/enhancement

The changing and evolving knowledge visualization contexts often lead to varied visualization requirements, which, in turn, require knowledge visualization systems to enable users to flexibly modify/customize/ enhance visualizations. A visualization, which can fulfil a particular purpose at one point in time, may not be able to achieve the same level effectiveness when the visualization stakeholders, purposes and/or contexts change over time. Thus, knowledge visualization systems need to offer users the capabilities of flexibly modifying, customizing and enhancing visualizations so as to meet the changing requirements. This requirement can be further clarified by applying Chi and Riedl (1998)’s data state model. Chi (2000) opined that a visualization technique can be decomposed into a set of data stages and operations. Data operations are composed of within-stage operators (i.e. value, analytical and visualization stage operators) and between-stage transformations (i.e. data, visualization and visual mapping transformations). Visualization modification/customization/ enhancement can be conducted through adjusting these within-stage and between-stage operations, e.g. selecting the desired visual representations, changing the colour or the hue, adjusting transformation parameters, etc.

3.3 Visualization integration

This requirement is concerned with flexibly combining the visual contents generated by different visualization techniques so as to present a rich view of the underlying data. Due to the changing visualization purposes, contexts and stakeholders, visualizations are often required to reveal different features of the source data. However, visualization techniques have their specific focus on handling particular types of data and reflecting particular features of the source data (Chi et al., 1997). In other words, no single visualization technique can be effective for addressing all data types and/or all visualization purposes. Therefore, integrating multiple visualization techniques within a single visualization system becomes a natural and effective way to assist users in exploring more features of the source data (Hibbard, 1999). Visualization integration may need to be performed against a single data source or multiple sources.

3.4 Visualization transformation

Besides creating and customizing visualization techniques, visualization transformation is equally important for maintaining the effectiveness of a visualization in terms of fulfilling a certain purpose. It requires visualization systems to allow users to transform visualizations from one type to another in a flexible and seamless manner with the minimum amount of effort required. This will enable the users to visualize the same set of data through different visualization techniques and observe different features/views of the data. In order to fulfil the requirements posed above, we developed and proposed a CAVE framework (section 4) which is further elucidated through an implementation (section 5).

4. Contextual Adaptive Visualization Environment framework

The Contextual Adaptive Visualization Environment (CAVE) framework builds upon the CAVE model discussed in the previous section. As illustrated in Figure 4, a knowledge visualization solution comprises four fundamental building blocks, that is, data, models, solvers and scenarios. These building blocks together assist a knowledge worker in translating a decisional problem into a form that is recognizable and manageable by CAVE and ultimately by a knowledge worker. This understanding enables the knowledge worker to create visualization oriented data, models, solvers and scenarios

Xiaoyan Bai et al.


and adapt them into a form that effectively responds to the contextual changes. These components are managed and connected together by a central component – kernel – which enables the communication among different components. All these components cooperate together to help with various tasks involved in knowledge generation, knowledge representation, knowledge presentation, visualization interaction and visualization evaluation. CAVE may incorporate two broad types of data, that is, user data required by the system execution, and the data depicting the characteristics of problem, purpose and knowledge worker contexts. They also involve two essential groups of models for accomplishing knowledge creation and visualization. Accordingly, there are two types of solvers for manipulating their corresponding type of models. Data, model and solver can be integrated to form a scenario. Among these CAVE components, the problem related data, models, solvers and scenarios are used to generate knowledge while the visualization technique related components manages the representation and presentation of the knowledge. More specifically, the problems related components are responsible for enhancing the quality, relevance and effectiveness of the source data in terms of how well they can address the decisional problem of interest. In contrast, the visualization technique related components define and manage the way of how the ready to be visualized data sets are transformed into appropriate views so as to adapt to the dynamic contexts. A knowledge visualization solution is made up of appropriate problem and visualization technique scenarios. This framework is used to guide the design and implementation of a contextual adaptive visualization environment, which is further elucidated in the subsequent section.

Figure 4: Contextual adaptive visualization environment framework

5. Implementation

To validate the concepts, models and framework of CAVE, we implemented a vertical prototypical system against the CAVE framework through utilizing a set of Microsoft technologies, i.e. Bing map, windows presentation foundation, ADO.NET entity framework, and SQL Server. The prototype enables the sensing of contextual changes through accessing a number of historical and/or real-time



data streams. Apart from monitoring and communicating with these data streams, the system also supports the creation of problem and visualization scenarios that enable a knowledge worker to sense and become aware of emerging situations. The impact from the contextual changes is reflected by the adjustment of visualization requirements. The prototype helps the knowledge worker to respond to the contextual changes through refining or re-creating knowledge visualization solutions, for example, mapping the problem scenario to a more appropriate visualization scenario to better fit in the new knowledge visualization context. To help with demonstrating the support of the prototype, we introduce two cases, that is, Napoleon’s army march to Russia, and child statistics. The former case resides more in the domain of historical data management while the latter is mainly about statistical data analysis. In the Napoleon’s march case, we focus on exploring the relationships between army size reduction and its potential causing factors such as temperature, speed, location altitude, enemy size and available resources at each location, etc. In the child statistics case, we concentrate on discovering patterns that exist among a variety of education related indicators in different countries, e.g. primary school completion rate, expenditure per student, and literacy rate of adult. Both cases require visualizing spatial temporal multi-dimensional data. The following two sub-sections illustrate the support of the CAVE prototype at both macro level where the problem situation changes from the Napoleon’s march case to the child statistics case and micro level where different knowledge workers expose different visualization preferences.

5.1 Macro level contextual change

When the problem situation changes from one case to another, the CAVE prototype allows knowledge workers to create different problem and visualization scenarios for different cases. For visualizing the invasion and retreat related information of Napoleon’s main troop, in 1869 Charles Joseph Minard published a map to portray the defeat of Napoleon’s army in Russia (Tufte, 1997). Building on top of the Minard’s work, we created an integrated problem-visualization scenario (Figure 5) to illustrate how the army size (indicated by the width of the route band) diminishes as the temperature and moving speed change vary along the route in an animated fashion (Figure 6). In contrast, the problem-visualization scenario (Figure 7) we created for the child statistics case presents the trends of multiple education indicators in a static way (Figure 8).

Figure 5: An integrated problem-visualization scenario for Napoleon’s march case

5.2 Micro level contextual change

Knowledge sharing among different knowledge workers can require the system to accommodate their diverse visualization requirements and preferences. For example, some knowledge workers may prefer to use colour to present a high level overview of the child/education indicators to help with their comprehension of the knowledge. In contrast, others may like to watch and/or listen to the related media bites of the child/education indicators through vivid video/audio files. An example of a three-layer integrated problem-visualization scenario is demonstrated through Figures 9-11. Figure 9 shows four indicators for each country, i.e. female children out of primary school, male children out of primary school, literacy rate of female adults, and literacy rate of male adults. These indicators are represented by the following colours, i.e. red, green, blue, and yellow, in respective. For each indicator, deeper colours indicate higher values and lighter colours mean lower values. By zooming into a detailed level, the information about how the four indicators vary across consecutive years in different countries is presented in line graphs in Figure 10. The comparison among indicators enables knowledge workers to roughly infer whether a certain relationship among multiple indicators may exist. By zooming into a more detailed level, the users may play available videos and/or audios

Xiaoyan Bai et al.


associated with an indicator in a particular country so as to obtain rich contextual information (Figure 11).

Figure 6: An animated visualization for exploring causes for Napoleon’s army death

Figure 7: An integrated problem-visualization scenario for child statistics case



Figure 8: A static visualization for aiding the pattern discovery of child statistical data

Figure 9: Top layer - child related indicators by colours

Figure 10: Middle layer - showing trends of multiple Indicators by line graphs

Xiaoyan Bai et al.


Figure 11: Bottom layer - presenting rich Information by videos and audios

6. Conclusion

Visualisations are integral for the creation, transfer and sharing of knowledge. Knowledge visualization context is complex and dynamic in nature. Such complexity is caused by the extensive diverse and changing factors involved in the problem, purpose and stakeholder contexts. The dynamic and changing problem, purpose and knowledge worker contexts often lead to changing visualization requirements that are ill supported by the visualizations systems involved in knowledge management. One major challenge brought by the context complexity is how to enable knowledge workers to flexibly adapt knowledge visualizations to the changing and evolving knowledge visualization context and maintain their effectiveness over time and space. To help with addressing contextual dynamics and complexity, we delineated knowledge visualization context and proposed the concept of a contextual adaptive visualization environment. The ideas involved in CAVE were further explicated through CAVE models and framework. These proposed artefacts are validated through the implementation of CAVE. The CAVE prototype is demonstrated through how it supports contextual changes at both macro and micro levels. It deserves to be that the current prototype has only been tested against a limited number of knowledge visualization context changes. Identifying and categorizing representative contextual changes as well as exploring and improving the support offered by the CAVE prototype will be accomplished in our future research.

References

Bresciani, S. and Eppler, M.J. (2009) „Beyond Knowledge Visualization Usability: Toward a better Understanding of Business Diagram Adoption’, in Proceedings of the 13th International Conference on Information Visualization, Barcelona, pp 15–17.

Bresciani, S. and Eppler, M.J. (2010) „Choosing Knowledge Visualizations to Augment Cognition: the Managers’ View’, in IEEE Proceedings of the International Conference on Information Visualization, London.

Burkhard, R. A. (2004) „Learning from Architects: The Difference between Knowledge Visualization and Information Visualization’, in Proceedings of the 8th International Conference on Information Visualization, pp 519–524.

Cañas, A., Carff, R., Hill, G., Carvalho, M., Arguedas, M., Eskridge, T., Lott, J. and Carvajal, R. (2005) „Concept Maps: Integrating Knowledge and Information Visualization’, In Tergan, S.O. and Keller, T. (Eds.) Knowledge and information visualization: Searching for synergies, Springer-Verlag, Heidelberg, vol 3426, pp 205–219.

Card, S.K., Mackinlay, J.D. and Shneiderman, B. (1999) Readings in Information Visualization: Using Vision to Think, Morgan Kaufman Publishers, San Francisco, California.

Chen, C. (2006) Information Visualization: Beyond the Horizon, 2nd Edition, Springer, London. Chen, G. and Kotz, D. (2000) A Survey of Context-Aware Mobile Computing Research, Technical Report,

Department of Computer Science, Dartmouth College. Chi, E.H. (2000) „A Taxonomy of Visualization Techniques Using the Data State Reference Model’, in

Proceedings of IEEE Symposium on Information Visualization, pp 69–75.



Chi, E.H. and Riedl, J.T. (1998) „An Operator Interaction Framework for Visualization Systems’, in Proceedings of IEEE Symposium on Information Visualization, pp 63–70.

Chi, E.H., Barry, P., Riedl, J. and Konstan, J. (1997) „A Spreadsheet Approach to Information Visualization’, in Proceedings of IEEE Symposium on Information Visualization, pp.17-24.

Dey, A.K. (2001) „Understanding and Using Context’, Personal and Ubiquitous Computing, vol 5, no. 1, pp 4–7. Donald, C., Blake, A., Girault, I., Datt, A. and Ramsay, E. (2009) „Approaches to Learning Design: Past the Head

and the Hands to the HEART of the Matter’, Distance Education, vol 30, no.2, pp 179–199. Dreyfus, H.L. and Dreyfus, S.E. (1986) Mind over Machine: the Power of Human Intuition and Expertise in the

Era of the Computer, Free Press, New York. Eppler, M.J. and Burkhard, R.A. (2007) „Visual representations in knowledge management: framework and

cases’, Journal of Knowledge Management, vol 11, no. 4, pp 112–122. Eppler, M.J. and Burkhard, R.A. (2011) „Knowledge Visualization’, in Schwartz, D. and Te'eni, D. (Eds.),

Encyclopedia of Knowledge Management, 2nd Edition, pp 987–999. Heer, J., Bostock, M. and Ogievetsky, V. (2010) „A Tour through the Visualization Zoo’, Queue, vol 8, no.5, pp

20–30. Hibbard, B. (1999) „Top Ten Visualization Problems’, ACM SIGGRAPH Computer Graphics, vol.33, no.2, pp.21-

22. IBM Many Eyes (2011) „Visualization Options Available in Many Eyes’, [online],

http://manyeyes.alphaworks.ibm.com/manyeyes/page /Visualization_Options.html. Lee, A.C., Lee, J.C. and Lee, C.F. (2009) Financial Analysis, Planning & Forecasting: Theory and Application,

2nd Edition, World Scientific Pub Co Inc, Singapore, pp 2-3. Nivala, A.M. and Sarjakoski, L.T. (2003) „Need for Context-Aware Topographic Maps in Mobile Devices,’ in

Proceedings of the 9th Scandinavian Research Conference on Geographical Information Science, Espoo, Finland, pp. 15-29.

Petit, M., Ray, C. and Claramunt, C. (2006), A Contextual Approach for the Development of GIS: Application to Maritime Navigation, in Proceedings of the 6th International Symposium on Web and Wireless Geographical Information Systems.

Pinaud, B., Kuntz, P., Guillet, F. and Philippé, V. (2006) „Graphical visualization in the knowledge management system Atanor’, Journal of Universal Computer Science, pp 481–488.

Schilit, B., Adams, N. and Want. R. (1994) „Context-Aware Computing Applications’, in Proceedings of IEEE Workshop on Mobile Computing Systems and Applications, Santa Cruz, California, pp 85–90.

Schmidt, A., Aidoo, K.A., Takaluoma, A., Tuomela, U., Van Laerhoven, K. and Van de Velde, W. (1999) „Advanced Interaction in Context,’ in Proceedings of 1

st International Symposium on Handheld and

Ubiquitous Computing, Karlsruhe, Germany, pp 89–101. Shneiderman, B. (1996) „The Eyes Have It: A Task by Data Type Taxonomy for Information Visualization,’ in

Proceedings of IEEE Symposium on Visual Language, pp 336–343. Spence, R. (2007) Information Visualization: Design for Interaction, 2nd Edition, Prentice Hall. Stanford, X. (2001) „Map Your Knowledge Strategy’, Information Outlook, Special Libraries Association, vol 5,

no.5, [online], http://www.sla.org/content/Shop/Information/infoonline/2001/jun01/stanford.cfm. Tufte, E.R. (2006) Beautiful Evidence, Graphics Press, Cheshire. Wu, Z. and Chen, Y. (2009) „Context Awareness and Modeling In Self-Adaptive Geo-Information Visualization’, in

Proceedings of the 24th International Cartographic Conferences, Santiago.

http://manyeyes.alphaworks.ibm.com/manyeyes/page%20/Visualization_Options.html

http://www.sla.org/content/Shop/Information/infoonline/2001/jun01/stanford.cfm

ISSN 1479-4411 15 ©Academic Publishing International Ltd

Reference this paper as: Bredl, K, Groß, A, Hünniger, J, and Fleischer, J. “The Avatar as a Knowledge Worker? How Immersive 3D Virtual Environments may Foster Knowledge Acquisition” The

Electronic Journal of

Knowledge Management Volume 10 Issue 1 (pp15-25, available online at www.ejkm.com

The Avatar as a Knowledge Worker? How Immersive 3D Virtual Environments may Foster Knowledge Acquisition

Klaus Bredl, Amrei Groß, Julia Hünniger and Jane Fleischer Institute for Media and Educational Technology, University of Augsburg, Germany [email protected] [email protected] [email protected]

[email protected]

Abstract: The rapid development of virtual worlds has created new possibilities for supporting formal and

informal knowledge acquisition and learning processes online. Consequently, greater immersion of “knowledge workers” in cooperation and communication tasks in social virtual worlds should be a more prominent topic in sociological and cognitive-psychological research designs. The relatively new social potential of virtual worlds can be examined using theoretical models that describe the use and assessment of virtual world technologies in contexts of knowledge acquisition and exchange. In this paper, three co-created scenarios will be described to help demonstrate how virtual worlds can be used to explore new forms of interaction in (virtual) social contexts. These scenarios and the results of the avatar-based ethnographic investigation during the process of co-creation and collaboration will be introduced and used to reflect on the 3D projects. Afterwards, two sets of criteria to evaluate 3D environments for learning and teaching will be presented. The paper ends with suggestions for further research concerning the effects of immersion during collaboration and education in virtual worlds and an outlook on other upcoming 3D projects. Keywords: virtual worlds, immersion, knowledge exchange social software, knowledge management, Web 2.0, Second Life

1. Introduction

New architectures, new interconnected spaces, new open standards: The probability of internet users having some sort of presence within avatar-based virtual worlds or Multi User Environments (MUVE) – whether professional or personal – has seemingly increased (Gartner, 2007). Digital Environments are vital parts of the emerging Web3D. And a good thing too: Drawing on their possibilities for communication and cooperation, virtual worlds might be able to form a stronger internalization of IT-supported knowledge processes thereby creating new opportunities for formal and informal processes of knowledge acquisition – supporting a personalized knowledge management strategy (Hansen, Nohria & Tierney, 1999), making knowledge literally tangible and creating experience of action rather than theory-based knowledge. To achieve all of this, 3D worlds offer a wide range of tools that provide users with various possibilities to communicate and connect, ranging from simple text chat to professional collaborative interaction within social groups. Additionally, MUVEs seem to possess a special motivational immersive character. They allow their users to literally “plunge” into the virtual world, to be surrounded by a completely different reality, to experience immersion (Murray, 1997). Immersion is defined by the degree to which people perceive that they are interacting with their virtual environment rather than with their physical surroundings (Guadagno et al., 2007). Therefore, the use of social virtual worlds could help to achieve a higher involvement of “knowledge workers” in IT-based tasks of communication and cooperation. Different theoretical models of social presence and immersion examine the newly recognized social potential of virtual worlds supporting knowledge-based processes (Davis et al., 2009; Eschenbrenner et al., 2008). For example the Sociable Media Group at the MIT does research on special “Information Spaces” (Harry & Donath, 2008). Other researchers are analyzing the use and the assessment of virtual world technologies in the context of knowledge acquisition and exchange (Wittmer & Singer, 1998). Not only does the perception of the presence of one’s own avatar within a virtual environment for learning and teaching contribute to a higher degree of immersion, but also – and more so – the perception of the presence of others (Bredl & Herz, 2010; Davis et al, 2009). The user is not alone

mailto:[email protected]�






“out there”. There are people working and interacting with him within the same space - a fact that greatly increases the feeling of reality rather than virtuality. This creates a sense of actually “being there” (Heeter, 1992). However, the possibility of offering a higher degree of social presence and therefore creating higher levels of immersion raises further community-related questions: Could the requirements of the social constructivist and the even newer connectivistic paradigm (Siemens, 2005) be fulfilled through interconnected 3D spaces? Can virtual worlds support immersive knowledge communication? And even more importantly, which criteria must be met to successfully teach and learn within these environments? Since they are large, scalable, flexible, informal and non-structured, virtual worlds do indeed provide a constructivist and connectivistic environment – at least at a first glance. Their possibilities to support immersive knowledge communication are almost unlimited and should be further discussed in sociological and cognitive-psychological research designs. Apart from functions such as socializing via chat, gaming, role playing and movie showing, 3D virtual worlds offer a wide range of possibilities for interaction and collaboration. It is hardly surprising their application in education (edutainment), especially for children and teens aged 6 to 18, is increasing steadily (KZERO, 2011). This creates a whole new generation of experienced avatar users, eager to work professionally within virtual worlds in the decades to come. The following article illustrates some first steps to tap into this great potential by introducing three co-created 3D projects and suggested criteria to successfully evaluate avatar-based virtual learning and teaching settings. Theoretical Approach for Immersive Knowledge-Based Virtual Environments The idea of using virtual environments to foster knowledge communication is strongly supported by theory: There are indications of a connection between virtual worlds and intrinsic motivation. Game Research points to an increase in motivation and efficiency of the knowledge acquisition process due to the flow effect (Csikszentmihalyi, 1993) fostered by virtual spaces (Fritz, 2004). According to Csikszentmihalyi and Rathunde (1993), flow in virtual environments can be characterized by ten factors: loss of self consciousness, concentration, goal orientation, distorted sense of time, direct feedback, balance between ability level and challenge, a sense of personal control over the activity, intrinsic reward of the activity, lack of awareness of bodily needs and absorption into the activity. Consequently, it can be assumed that the flow effect may be achieved at the peak of immersion (Krause, 2008). The typical forms of communication and collaboration within 3D virtual worlds – interacting with the help of customized avatars in three-dimensional graphical settings, bringing content to life, sharing and using 3D objects – apparently cause a higher degree of immersion (Fromme, 2006) and therefore lead to a more intense participation and a higher degree of interaction within a group of online learners. Increased understanding of the effectiveness of 3D virtual worlds as learning scenarios is leading to them being held in higher estimation and being taken more seriously (Nattland, 2008). Virtual worlds have become more than just a game. They are being used to teach, counsel, prepare, support and even heal – the U.S.Army, for example, currently operates a virtual island within the world of Second Life, giving help to soldiers’ families and soldiers suffering from Post Traumatic Stress Disorder (United States Army, 2011). However, the user has to be willing to learn and work in three-dimensional worlds. As Bartle (2003) suggests, immersion is always influenced by a user’s subjective perception and personal attitude towards a virtual construct. Participants of social knowledge-based processes within virtual worlds usually do actively seek knowledge exchange and participation. They develop relationships and, bit by bit, begin to see themselves as part of a community. Building upon digital social networks, 3D knowledge spaces add a dimension of high immersion (Castronova, 2005) to knowledge exchange processes, favouring the development of communities of practice (see Wenger, 1998; Wenger et al, 2005). Therefore, avatar-based 3D environments could serve as a platform for user-centred knowledge acquisition and

Klaus Bredl et al.


cooperation scenarios. Work-based social role-playing, simulations, and product-based experimental grounds are now easily achievable. It has become possible to use virtual worlds as instruments to exchange implicit knowledge and to enable an informal and constructivist knowledge exchange based upon the new learning paradigm of connectivism (Siemens, 2005). As a consequence of the developments in modern technology, the borders between real and virtual worlds are becoming increasingly blurred. Technological developments enable professionals to create photorealistic virtual environments, true-to-life sound quality as well as the ability to input devices that provide haptic feedback and capture the player’s movements (for example the Nintendo Wii or Microsoft Kinect). As a result, modern technology allows users to interact virtually in a way that very closely resembles “real” interaction. Thus, the player of modern computer games can experience higher levels of presence leading to the sense of actually being in the virtual environment (Steurer, 1993). Depending on how large and how vivid a virtual world is, the user can immerse more or less deeply (Pietschmann, 2009). A well-designed and complex environment has the potential to put the users into a state of mind where they are surrounded “by a completely other reality […] that takes over all [their] attention, [their] whole perceptual apparatus. [They] enjoy the movement out of our familiar world, the feeling of alertness that comes from being in this new place, and the delight that comes from learning to move within it” (Murray, 1997: 98ff). The real world outside becomes irrelevant. All of the user’s thoughts are focused on the virtual reality around them. Immersive virtual environments enable the user not only to learn, but also to use and improve what they have learned – to gain experience through action. According to Gee (2009: 70),

“people primarily think and learn through experiences they have had. They store these experiences in memory […] and use them to run simulations in their minds to prepare for action and problem solving in new situations. These simulations help them form hypotheses about how to proceed in the new situation based on past experiences.“

In other words: What people have learned online and in a virtual world can help them solve similar real-world exercises and problems.

2. Social virtual worlds in enterprises

Contrary to the rigid codification of knowledge elements in systems and platforms, the strategy of personalization within knowledge management (Hansen, Nohria & Tierney, 1999) is gaining in importance. Due to the introduction of Web 2.0 technology and social software, this development, known as Enterprise 2.0 (Koch and Richter, 2007), corresponds with a strategy of personalization, which is seeing an increase within enterprises. The staff of IBM, for example, are already using the Second Life GRID-Engine for internal cooperation and learning processes (Fray & Carey, 2009). In the future, “Enterprise Immersive Platforms” (Driver & Driver, 2008) could be used enterprise-wide. This raises the question of how the new factor of immersion could be inserted in a model for social Information- and Knowledge Systems. Fig. 1 illustrates how the factor of immersion could be embedded into a social software model for use in organizations (Bredl, 2009). In this crystal-model for immersion in Social Media for knowledge processes we find, in the center, the Social Software technologies: Weblogs and Micro-blogs, Content and Document Management Systems (CMS, DMS), Wikis, Digital Social Networks and finally Avatars in Virtual Networks. On the left side there is the strategy of codification, which leads to Content and Information Management. On the right side, in the human area, the strategy of personalization, which, combined with an high degree of immersion, leads to the need to manage presence. Further Management functions are communication and community management.



Figure 1: Crystal (Xtal-)-Model –Immersion in Social Media

3. Scenarios for the application of knowledge and learning settings in a 3D virtual environment

During the course of three semesters, students of Media and Communication co-created various prototypical scenarios, which combine aspects of game-based learning and simulations within the virtual worlds of Second Life and Open Simulator (2011). The initial motivation for developing these scenarios was a result of the recognized limitations of digital support for learning, teaching and knowledge management via traditional platforms such as Stud.IP, Moodle and various other learning management systems as well as the limitations of synchronous interaction during online courses. Three scenarios, their infrastructure and implementation, their potential benefits and the challenges which were overcome during creation will be described within the following section.

3.1 Surrounding the case study

In order to create content within virtual worlds, an appropriate plot of virtual land was needed. The Digital Media division at the Augsburg Institute of Media and Educational Technology found their plot on the European University and the neighbouring European Science Island (Simteach, 2011); two spaces within the virtual world of Second Life especially designed for academic 3D projects. Scenario 1 - Learning adventure In the first project, a game accompanying the course “Introduction to the Methods of Empirical Communication Research” at Augsburg University was created. Designed just like an adventure game and fully implemented in Second Life, its frame story followed the theme of well-known fairy tales. As with researchers at the “Magic Wood Research Institute”, students explore the Magic Garden, Sleeping Beauty’s Castle, the Gingerbread House and even Rapunzel’s Tower while answering questions about empirical communication research and completing different tasks (Bitzer et al, 2010; Bitzer & Bredl, 2010) (see figure 2). But does a university need adventure games in order to impart scientific knowledge? When thinking about education at university, one might easily picture a professor lecturing in front of hundreds of students taking notes. One might also think of seminars where students give talks one by one, their only motivation being to gain urgently needed credit points. It is possible that these students feel more

(Social) Presence

Management

Content and Information

Management

Communication Management

Community Management

Social

Tagging Virtual

Worlds Wiki

Strategy of Codifcation

Strategy of

Personalisation

Strategy of

Personalisation Strategy of Codifcation

Avatar High Degree

of Immersion

Low Degree

of Immersion

Human Informatio

Weblog Micro-blog -

Digital

Social Network

CMS, DMS

Klaus Bredl et al.


like they have to learn the subjects rather than they want to learn them. For children, there is a wide range of learning games, many of which make learning a more attractive venture. However, for university students, there are only a very small number of similar products – there are far too many subjects and courses, differing from university to university, to allow the creation of economically interesting learning software.

Figure 2: Learning adventure „Magic Forest“

Looking at virtual worlds like Second Life, where everyone is able to create content without needing to know how to program and without having to spend a lot of money, the alternative is obvious: here, teachers and instructors can easily bring their own ideas and their own game concept to life, thereby allowing theory-based courses and lectures to contain experience of action. At the moment, there are two main e-learning settings to be found in Second Life. First, there are virtual classrooms where people meet and listen to presentations and lectures. Second, there are simulations and pre-made parts, allowing the user to experience a subject close up. However, virtual worlds offer many more possibilities to support learning and teaching. One of them is to connect 3D environments such as Second Life with the aspects of game-based learning and so-called “serious games” (Abt, 1970; Gee, 2003). The game accompanying the course “Introduction to the Methods of Empirical Communication Research” for students of Media and Communication does just that. Its use shall be described in the following section. By definition, students of Media and Communication have a high affinity with digital media. Some of them are even already using Second Life – a good premise for learning and creating in virtual worlds. The seminar “Introduction to the Methods of Empirical Communication Research” is a blended-learning seminar with twelve face-to-face meetings through the course of one semester. Every week introduces a different theoretical focus. During the real-world meetings, students gain the basic knowledge allowing them to complete the exercises within the game. Further information is given in Second Life. In the “Magic Forest”, students have to unravel hidden secrets and complete various quests to consolidate their knowledge. Every level is represented by a new fairy tale. Once there, students have to answer questions that draw on the topic of the last meeting and afterwards work on a practical exercise such as conducting an interview. Every face-to-face meeting unlocks a new level in the virtual “Magic Forest” and every new level is more complex than the previous one. In order to complete their in-game tasks, students have to work together through face-to-face meetings in groups to create questionnaires and interview guidelines. If a problem cannot fully be solved in Second Life – for example because it requires statistical software – the task is then worked out in the real world and the results presented later on in Second Life. After the next meeting, if the exercise is correct, they will get the password for the next level and can then teleport there to advance.



Scenario 2 - Knowledge acquisition in disaster training When disaster hits, it hits unexpectedly. And when it does, well-trained emergency professionals within the police force, the fire brigade and rescue services have to cooperate across unit borders and make prudent decisions in the midst of chaos in order to save lives. But how can professionals prepare for large scale emergencies when hundreds of injured persons need to be taken care of by far too few emergency physicians; when important infrastructure is destroyed, damaged or flooded? Regular training cannot prepare emergency professionals for all of this. Efficient disaster training requires a large number of emergency professionals and actor “injured persons” to be realistic. Preparing such training exercises tends to be extremely time-consuming, difficult and expensive: The German Disaster Management Exercise “Lükex”, for example, simulated 14 different disaster sites all across the nation, uniting tens of thousands of emergency professionals in one large scale training exercise. It took two years of planning and cost almost one million Euros. Each participating unit also had to remain deployable during the whole 36-hour training session in case of a real emergency. Therefore, during a second project, the Digital Media division at the Institute of Media and Educational Technology at the University of Augsburg built up a prototypical disaster training site showing how real world professionals might gain the possibility to create training at the point of need – allowing them to practice the management of real world emergencies without the limitations of real world training, such as time and place (Groß, 2011; Groß, et al. 2011). Since – as illustrated above – real world disaster training tends to be extremely time-consuming, expensive and difficult to organize, disaster training exercises are rare. There is a great need for efficient and affordable training solutions that allow emergency instructors to create “out-of-the-box” training scenarios just at the point of need. The project team tried to create just that. They built up a prototype of an online training site within Second Life, simulating a cargo plane crash over a suburban area on European University Island (see figure 3). There, the individual player has to manage multiple spreading fires and perform a triage following the Simple Triage and Rapid Treatment Scheme StaRT. Every crash victim has to be sorted into four different categories of injuries ranging from T3 (minor injuries) to T1 (major injuries) and TOT (fatal injuries or dead). The player has to decide quickly which individuals need to be taken care of immediately and which can wait until the severely injured have been treated? To guide the player in making the correct treatment decision, every injured person within the simulated disaster site has visible, more or less severe injuries and gives away a notecard. This notecard describes the individual’s injuries and afflictions. After making a decision, the player has to put up the appropriate triage sign and move on. The lack of immersive medical examination methods such as vitality checks is deliberate in order to allow for easy-to-use controls. These aspects are part of First Aid Training and First Care and should rather be practiced offline.

Figure 3: Emergency training in 3D

Klaus Bredl et al.


When fighting fire, the player is forced to pay attention. They cannot enter burning areas or buildings filled with smoke without proper breathing protection and fire-resistant clothing – if they do, they will become casualties themselves and make the actual task at hand even more difficult for their remaining team members. All of the player’s actions during Second Life disaster training may be filmed with screen capturing tools such as “Fraps” and can be evaluated afterwards either in-world or in the real world. Scenario 3 - Counseling in virtual worlds Virtual worlds are more and more becoming part of our reality and have an increasing influence even on the area of psychology and counselling. But can virtual worlds really support clients of psychosocial counselling and if so – how? First of all, virtual environments may be able to enhance a client’s identity by allowing the user to create a virtual self – within the virtual world they can be a totally different person, interacting freely with other people, overcoming real-world fears and limitations in a game of identity (Misoch, 2006; Schelske, 2007). Secondly, virtual worlds could improve and support regular psychosocial counselling sessions in general. Due to its guaranteed absolute anonymity and the possibility to simply log out whenever the client wants a session to end, virtual worlds allow counsellors to get in touch with people who otherwise would not seek psychosocial counselling. However, there are very few efforts being made to use immersive virtual environments for counselling in the new Web3D. In 2009, the university of Neubrandenburg in Germany together with one of the authors took a first step towards psychosocial counselling within virtual 3D environments: It created a prototypical counselling setting in Second Life (Bräutigam et al, 2011). This psychosocial counselling ambulance, built onto the university’s virtual campus in Second Life, consists of an information area as well as three different counselling settings in various skyboxes that are accessible via teleportation. During building, a special focus was set on creating a pleasant and confidence-inspiring environment in order to allow for an appropriate and positive counselling area. The Neubrandenburg virtual psychosocial counseling ambulance features a neutral conference room, a magical forest and a Japanese garden. Since every client is allowed to choose the counselling setting they like best, the client is offered an active role during counselling right from the beginning (Bräutigam et al, 2011). After building up the ambulance, a campaign across various social media services was started to get the first “real” anonymous clients in order to investigate the possibilities and limitations of virtual psychosocial counselling. Clients with deeper psychological problems and those suffering from psychiatric illnesses were excluded from the study via sounding interviews. During counselling, the counsellor and the client were accompanied by a team of students reflecting on the use psychosocial counselling within 3D virtual worlds. Results showed significant interdependencies between the clients’ real and their “second life”. Within virtual worlds, clients were observed talking very freely about their real life difficulties and necessities, suggesting systemic counselling in virtual worlds to partly act as trans-cultural counselling.

4. Lessons learned in projects

After creating some 3D virtual world projects, the team can look back at various lessons learned. Generally, students responded very positively to the projects, as they were very motivated to learn and interact within 3D virtual environments. During sessions, they developed many new ideas about how to use virtual worlds for educational and cooperative purposes themselves. The fact that students often stayed within the virtual environment to continue their discussions even after the sessions ended was very remarkable. This strengthens the hypothesis of the existence of processes of immersion and flow (Csikszentmihalyi & Rathunde, 1993). However, there are some limitations to the use of virtual worlds, such as the lack of possibilities to combine the established learning and communication platforms and the new possibilities of knowledge acquisition and exchange in 3D environments. The projects showed that more time should be taken to develop instruments that take advantage of the steady nature of the learning, simulation



and counselling environment – introducing for example elements such as bots that interact with the user even when neither teacher nor tutors are online. Despite more interactive possibilities, the need for the possibility to create and share content became evident during the projects. Furthermore, learning and knowledge objects created within conventional web applications could not easily be transferred into virtual worlds and vice versa. The project Sloodle, which combines Second Life and the open source platform Moodle, is one of only few exceptions (Kemp & Livingstone, 2006). It is a combination of the 2D-web and the 3D-environment; a Second Life and Moodle mash-up. In order to successfully create immersive virtual environments for learning, counselling and teaching, various guidelines and criteria need to be fulfilled. However, official recommendations or guidelines concerning didactic design within social virtual worlds have yet to be voiced. Based upon the introduced case studies and 3D projects, the Digital Media division of the Augsburg Institute of Media and Educational Technology has developed a set of criteria for immersive learning environments (Dörr, 2010). Featuring a tabular design resembling the Zurich pedagogical university’s inventory, nine main categories were created:

“Getting Started” and Support

Content Design

Didactic Design

3D Design

Design of Tasks and Questions

Immersive Dimension

Motivation & Emotion

Communication & Cooperation

Results

Some of the categories contain special filter-questions, guiding the evaluator or designer to sub-categories relevant for his very own learning or counselling setting. This allows the quite extensive set of criteria to be shortened wherever possible, concentrating on the necessities and thereby greatly increasing the practical utility of the set. However, some settings such as the emergency training site mentioned above, require special elements and therefore a special set of criteria of their own. Because of this, there have been worked out a special set of criteria with the needs of professional emergency instruction in mind (Groß, 2011). Virtual worlds for training professional emergency responders within the emergency services need to be highly specialized learning environments, fulfilling several special requirements in order to guarantee successful training. However, there are no guidelines to allow emergency instructors to evaluate the suitability of existing training software. Groß (2011) tried to bridge this gap by creating a set of criteria based on didactic theory and interviews with emergency instructors, keeping the special requirements of professional emergency training in mind. This newly developed set of criteria rests upon the works of Benkert (2001) and Thomé (1989) as well as the categories and items of Dörr (2010) and has been enhanced with various important emergency facts and requirements. Like this, 41 requirements for software-based training solutions for emergency professionals were developed. They are summarized within seven main categories relevant for the evaluation of disaster training software: The degree of reality and detail, the scalability, the design of tasks, environments and missions, the usability, the possibility to adapt to the user as well as the instructor and the focus onto tactical aspects. Within the set, those categories are represented as following:

“Getting Started”, Usability and Security

Content Design

Media Design

Design of Tasks and Missions

Aspects of the User

Klaus Bredl et al.


Aspects of the Instructor

Feedback

Across all nine categories, 38 criteria and 60 items can be used to evaluate the software in question. In order to allow for an easy and objective assessment of the future training tool, the evaluator only has to decide whether each item’s requirement is fulfilled within the virtual world (YES) or not (NO). The possibility to create own missions from scratch and some other closely defined aspects can be rewarded with Brownie Points. Every category ends with a summary of the achieved YES and Brownie Points, showing how the software totalled within this part of the set. In order to avoid a subjective bias of the results, the emergency set of criteria does not attach different weights to any of the items. This decision was taken very deliberately. Which of the given aspects are especially relevant is going to differ from instructor to instructor and from training goal to training goal. This special set of criteria therefore doesn’t tell what is important, but rather shows the software’s detailed score across all nine categories, allowing the instructor to decide whether the virtual world fulfils his requirements or not.

5. Conclusion and prospects

The goal of this paper was to describe the research phenomena in regard of social, cognitive and personal competencies in teams of knowledge workers that could be observed, and to explore new forms of interaction in virtual social contexts. Various scenarios were created to study the potential of virtual worlds for supporting knowledge and collaborative processes; three of them are featured in this article. All of the featured projects were based upon peer learning. Peer education is particularly suitable for use with information technologies since it creates cooperative learning via face-to-face interaction on the one hand and collaboration within virtual environments on the other. Thanks to multimodal online communication, the group can interact and communicate in various ways. Heterogeneous groups additionally benefit from peer learning, resulting in the individual knowledge workers learning from each other as well as teaching one another. The advantages of immersion and networking and the actual knowledge gain through the use of virtual worlds exceed the potential costs because of the amount of time and travel that are saved. According to Bartle (2003) the following points are to be considered in the application of MUVES:

The degree of (social) distance between the participants in a learning environment could be regarded as diminishing thanks to a growing immersion in 3D environments.

The more or less unlimited possibilities of interaction compared to conventional information and communication systems could be further objectives of research.

Changing the user’s own identity by appearance of diversity (ethnicity, gender) brings the possibility of anonymity and therefore equality in the knowledge process.

The feeling of being present together with other learners in the learning field is possibly the strongest factor in how immersion is perceived in the learning setting of a virtual world. This is also strengthened by the other users’ reactions to the digital self, which increases one’s own presence and participation within the MUVE.

Based upon open standards, some prototypical platforms for closed 3D intraworlds such as OpenSim, Croquet or Sun Wonderland are currently in further development. It is conceivable that virtual worlds designed especially for knowledge work could increase the quality of further immersive knowledge processes. Corresponding with development initiatives, there should be further effort to create interfaces connecting real and web-based environments. The conclusion may be drawn that immersive education tends to be more engaging than text- or video-based online communication. This leads to the hypothesis that the phenomena of immersion could increase the motivation and the learning capacities of avatar-based co-workers and learners. Therefore, the phenomena of immersion and community-building should be studied more extensively by means of reasonable operationalisations in experiments with prototypical scenarios in virtual worlds.



The Digital Media Division at Augsburg University continues working within virtual worlds, putting to the test the open-source world of OpenSim. Although the software is still in alpha status, various regions have been put up within OpenSim, allowing students of Media and Communication to put their very own 3D projects to life. The results are outstanding. Over the course of just one semester, 40 students have created a total of eight great learning sites within the “Second Learning Grid”

1

, featuring among others a bicycle training course for kids, the alchemist’s tower introducing chemistry or a oversized walk-in computer. Since OpenSim’s user interface, handling and scripting language are very similar to Second Life, students could easily apply what they had learned in Second Life to their tasks in OpenSim.

Currently, another group of students continues to build on a third region, extending the possibilities of “Second Learning Grid” by developing a virtual first aid training, a planetarium with the universe, and a walk-in painting of Monet – to mention only a few projects that are in the works.

References

Abt, C. (1970): Serious Games. New York. The Viking Press. Bartle, R. (2003): A Self of Sense, Selfware, [online], http://www.mud.co.uk/richard/selfware.htm Benkert, S. (2001): Erweiterte Prüfliste für Lernsysteme, [online], http://benkert-rohlfs.de/Promotion/ EPL.pdf Bitzer, C. & Bredl, K. (2010): Research-Adventure in the Magic Forrest, Presentation at the conference: „Virtual

World Best Practices in Education (VWBPE 2010)“, March 12-13. Bitzer, C., Bredl, K. & Adler, F. (2010). Educational Games in Web3D,Proceedingsofthe IADIS International

Conference Web Virtual Reality andThree-Dimensional Worlds 2010, IADIS Press. Bräutigam, B., Herz, D. & Bredl, K. (2011). Von Avatar zu Avatar, SystemischorientierteBeratung in virtuellen

Welten. In: Familiendynamik. Interdisziplinäre Zeitschrift für systemorientierte Praxis und Forschung. 36(1), 14-21.

Bredl, K. (2009): Immersive Education in Social Virtual Worlds. Poster presented at the 15th Americas Conference on Information Systems (AMCIS 2009) in San Francisco, USA (August 06th - 09th).

Bredl, K. & Herz, D. (2010): Immersion in virtuellen Wissenswelten. In: Th. Hug & R. Maier (Hrsg.), Medien - Wissen - Bildung: Explorationen visualisierter und kollaborativer Wissensräume. Innsbruck.

Castronova, E. (2005): Synthetic worlds: The business and culture of online games, The University of Chicago Press, Chicago, IL.

Csikszentmihalyi, M. (1993): Das Flow-Erlebnis, Klett-Cotta, Stuttgart. Csikszentmihalyi, M. & Rathunde, K.(1993): The measurement of flow in everyday life: Towards a theory of

emergent motivation. In J. E. Jacobs (Ed.): Nebraska symposium on motivation, Vol. 40: Developmental perspectives on motivation. University of Nebraska Press, Lincoln.

Davis, A., Murphy, John, O., Dawn, K., Deepak & Zigurs, I. (2009): Avatars, People, and Virtual Worlds: Foundations for Research in Metaverses.Journal of the Association for Information Systems,10 (2), 90-117, [online],http://aisel.aisnet.org/jais/vol10/iss2/1

Dörr, C. (2010): Bewertung von Lernszenarien in virtuellen Welten hinsichtlich der Unterstützung von Lernprozessen am Beispiel von Second Life. Unveröffentlichte Bachelorarbeit, Universität Augsburg.

Driver, E. and Driver, S. (2008): ThinkBalm. The Immersive Internet. Make Tactical Moves Today For Strategic Advantage Tomorrow, Immersive Internet Analyst Report Series, Issue #1.

Eschenbrenner, B., Nah, F. F.-H., & Siau, K. (2008). 3-D Virtual Worlds in Education: Applications, Benefits, Issues, and Opportunities. Journal of Database Management, Volume 19, Issue 4, 91–110.

Fray Carey, R. (2009): The Corporate Newsletter Goes Social. IBM and Employee-Centered Social Media.Web Guild Silicon Valley, [online], http://www.webguild.org/presentations/ibmsocialmedia.pdf.

Fritz, J. (2004): Das Spiel verstehen. Eine Einführung in Theorie und Bedeutung. Juventa, Weinheim. Fromme, J (2006): Zwischen Immersion und Distanz. In W. Kaminski, & M. Lorber (Eds.): Computerspiele und

soziale Wirklichkeit, kopaed, München. Gartner (2007): Gartner Says 80 Percent of Active Internet Users Will Have A "Second Life" in the Virtual World

by the End of 2011, Press Releases of 2007, [online], http://www.gartner.com/it/page.jsp?id=503861 Gee, J. (2009): Deep Learning Properties of Good Digital Games: How Far Can They Go? In: U. Ritterfeld, M.

Cody, P. Vorderer (Herausgeber) (2009): Serious Games. Mechanism and Effects, Routledge, Taylor and Francis, New York. pp. 67-83.

Groß, A. (2011): Virtuelle Welten für die Ausbildung von Einsatzkräften im Katastrophenschutz. w.e.b.Square, 03/2011.

Groß, A., Hoffmann, J. & Bredl, K. (2011): More than just a Game. Training Real-World Emergency Professionals in a Virtual World – A Prototype. Paper presented at the 4th Annual Conference Virtual Worlds Best Practice in Education (VWBPE), Inworld, 17-19 March 2011.

Guadagno, R. E., Blascovich, J., Bailenson, J. N. & McCall, C. (2007): Virtual humans and persuasion: The effects of agency and behavioral realism, Media Psychology (10) 1, pp. 1-22.

Hansen, M. T., Nohria, N. & Tierney, Th. (1999): What’s Your Strategy for Managing Knowledge? In: Harvard Business Review, 77(3-4), 106–116.

1 www.secondlearning.org

http://www.mud.co.uk/richard/selfware.htm�

http://aisel.aisnet.org/jais/vol10/iss2/1�

http://www.webguild.org/presentations/ibmsocialmedia.pdf�

http://www.gartner.com/it/page.jsp?id=503861�

Klaus Bredl et al.


Harry, D. and Donath, J. (2008): Information Spaces - Building Meeting Rooms in Virtual Environments. In Proc. of the SIGCHI conference on Human Factors in Computing Systems (CHI 2008), April 5-10 2008, Florence, Italy.

Heeter, C. (1992): Being there: The subjective experience of presence. In: Presence: Teleoperators and Virtual Environments, 1, 262–271.

Kemp, J. & Livingstone, D. (2006): Putting a Second Life “Metaverse” Skin on Learning Management Systems. In D. Livingstone and J. Kemp (Eds.): Proc. of the Second Life Education Workshop at SLCC, (p. 13–18). San Francisco, [online],http://www.sloodle.com/whitepaper.pdf.

Koch, M. & Richter, A. (2007): Enterprise 2.0 - Planung, Einführung und erfolgreicher Einsatz von Social Software im Unternehmen. Oldenbourg Wissenschaftsverlag, München.

Krause, D. (2008): White Paper Serious Games, Pixelpark Publikationen 2009, [online], http://www.pixelpark.com/de/pixelpark/_ressourcen/attachments/publikationen/080529_White_Paper_Serious_Games_English_final.pdf.

KZero (2011).The Virtual Worlds Universe.Virtual Worlds registered accounts Q1 2011. [online], http://www.kzero.co.uk/universe.php.

Misoch, S. (2006): Online-Kommunikation. UVK/UTB, Konstanz. Murray, J. (1997): Hamlet on the Holodeck: The Future of Narrative in Cyberspace. MIT Press, Cambridge. Nattland, A. (2008): Lernen in Second Life: Welten verbinden - Welten erfinden, Online TutoringJournal,[online],

http://www.online-tutoring-journal.de/ausgabejuli08/nattland1.htm Open Simulator (2011): What is Open Simulator? [online],http://opensimulator.org/wiki/Main_Page Pietschmann, D. (2009): Das Erleben virtueller Welten. Involvierung, Immersion und Engagement in

Computerspielen. Verlag Werner Hülsbusch, Boizenburg. Schelske, A. (2007): Soziologie vernetzter Medien. Grundlagen computervermittelter Vergesellschaftung.

Oldenburg, Munchen. Siemens, G. (2005): Connectivism – A Learning Theory for the Digital Age, elearnspace. Simteach (2011): [online] http://www.simteach.net/eui/. Steurer, J. (1993): Defining Virtual Reality: Dimensions Determining Telepresence, SRCT,Paper #104. [online],

URL: http://www.cybertherapy.info/pages/telepresence.pdf Thomé, D. (1989): Kriterien zur Bewertung von Lernsoftware. Mit einer exemplarischen Beurteilung von Deutsch-

Lernprogrammen. Hüthig, Heidelberg. United States Army (2011): DoD gives PTSD help 'second life' in virtual reality. [online],

http://www.army.mil/article/50751/ Wenger, E. (1998): Communities of Practice: Learning, Meaning, and Identity. Cambridge University Press,

Cambridge. Wenger, E.; White, N.; Smith, J. D. & Rowe, K. (2005): Technology for communities. CEFRIO Book Chapter.

[online],http://waterwiki.net/images/9/97/Technology_for_communities_-_book_chapter.pdf. Witmer, B. & Singer, M. (1998): Measuring Presence in Virtual Environments: A Presence Questionnaire.

Presence, 7, No. 3, 225-240.

http://www.kzero.co.uk/universe.php�

http://opensimulator.org/wiki/Main_Page�

http://www.simteach.net/eui/�

http://www.cybertherapy.info/pages/telepresence.pdf�

http://www.army.mil/article/50751/�

http://waterwiki.net/images/9/97/Technology_for_communities_-_book_chapter.pdf�

ISSN 1479-4411 26 ©Academic Publishing International Ltd Reference this paper as: Gaál, Z, Szabó, L, Obermayer-Kovács, N and Csepregi and A. “Middle Managers’ Maturity of Knowledge Sharing: Investigation of Middle Managers Working at Medium- and Large-sized Enterprises” The

Electronic Journal of Knowledge Management Volume 10 Issue 1 (pp26-38, available online at

www.ejkm.com

Middle Managers’ Maturity of Knowledge Sharing: Investigation of Middle Managers Working at Medium- and Large-sized Enterprises

Zoltán Gaál, Lajos Szabó, Nóra Obermayer-Kovács and Anikó Csepregi University of Pannonia, Department of Management, Veszprém, Hungary [email protected] [email protected] [email protected] [email protected] Abstract: Nowadays knowledge is becoming an increasingly important factor of organizational competitiveness. The way it is shared within the organization is essential and central not only to the success of organizations but also among those who share it, since those who take part in the knowledge sharing process also benefit from it. Since middle managers have an important position within the organization and play a significant role in the knowledge sharing process, this paper focuses on the knowledge sharing of those middle managers who work at medium and large-sized enterprises in Hungary. A new method of how to measure middle managers’ maturity of knowledge sharing is presented in this paper. Between 2007 and 2010 an empirical survey was conducted during which 400 middle managers working at medium- and large-sized enterprises in Hungary were investigated by a questionnaire. The answers of this survey have been analysed using Principal Component Analysis and four different principal components concerning the maturity of knowledge sharing have been identified. These four components are the availability among middle managers, the availability among the middle managers and their subordinates, the usefulness of knowledge among middle managers and the usefulness of knowledge among the middle managers and their subordinates. Keywords: knowledge sharing, maturity, middle managers, Hungary

1. Introduction

Knowledge sharing is considered to be a fundamental means through which organizational competitive advantage can be reached (Jackson et al. 2006). The way knowledge is shared within the organization is essential and central not only to the success of the organization where it takes place but also among those who share it, since those who take part in the knowledge sharing process also benefit from it. Middle managers play a key role in the knowledge sharing process. During the process of knowledge sharing middle managers’ roles have to change from control to mentor and facilitate others. However they often resist the realization of such changes. After building their careers and lives around the hierarchical pathway that exists within the organization, the appearance of a non-hierarchical work flow which does not require management behaviours concerning command-and-control may threaten them (Pommier et al. 2000). The fact regarding poor knowledge sharing and resistance towards middle managers’ knowledge sharing should not be neglected since it may cause serious damages within the organization.

2. Theoretical background

2.1 Middle managers

While in the 1970s Chandler (1977) emphasised that middle managers’ jobs cover exclusively the supervision of the lower hierarchical levels, now a large body of literature discusses their role in other fields. In the last 30 years there has not been a universally accepted definition regarding the term middle manager. Bower (1986:297-298) emphasises that middle managers are the only ones within their organization “who are in a position to judge whether issues are being considered in the proper context”. From another point of view Uyterhoeven (1989:136) argues that a middle manager is someone “who is responsible for a particular business unit at the intermediate level of the corporate hierarchy”. Ireland (1992) provides a more concrete definition regarding middle managers and describes them as employees working between an organization’s first-level and top-level managers. Furthermore their jobs contain the integration of “the intentions of top-level managers with the day-to-day operational realities experienced by first-level managers” (Ireland 1992:18). Regarding their







position in the organization Staehle and Schirmer (1992:70) emphasise that middle managers are “employees who have at least two hierarchical levels under them and all staff employees with responsibility for managing personnel”. According to Schlesinger and Oshry (1984) middle managers have to fulfil two major integrating tasks which are the investigation of top management and workforce, and their own integration across functional lines. Furthermore they believe that there is a connection between their commitments towards higher level of integration and the potential regarding the effectiveness of individuals and organizations (Schlesinger, Oshry 1984). Based on this Schlesinger and Oshry (1984) differentiated several possible integration levels containing the following categories: no integration, information sharing, assimilating information, joint planning and strategizing, mutual consultation, and power bloc. The literature of middle managers contains several other tasks that the managers need to fulfil which are the followings:

Balancing the demands and interests of those organizational members who are above and below them (Schlesinger, Oshry 1984);

Becoming adept at the integration of “hard” technical skills and “soft” skills (Barnes et al. 2001);

Possessing people skills since they have to work closely with other people within and outside the organization (Sayles 1993);

Balancing short- and long-term business demands (Schlesinger, Oshry 1984);

Being close enough to actual operations (Sayles 1993).

Previous studies investigating middle managers can be divided into two categories. One of them examined the middle manager - top manager relationship (Schilit 1987; Nonaka 1988; Dutton et al. 1997; Pappas, Flaherty 2003) while the others dealt with the middle manager - subordinate relation (Crouch, Yetton 1998; Xin, Pelled 2003; Glasø, Einarsen 2006). However in the following Figure by Kaplan (1984), in which the networks of managers are presented, it can be seen that middle managers are not only in vertical relationships with others but they are also in lateral relationships.

Figure 1: Sectors of manager’s networks (Kaplan 1984:38)

Zoltán Gaál et al


A similar concept regarding the relationships of middle managers can be found in Uyterhoeven’s (1989:137) statement as well, according to whom „the middle manager wears three hats in fulfilling the general management role” and these are being a superior, a subordinate and an equal. This is why they also have to manage relationships in several directions: upwards when they take orders; downwards when they give orders; and laterally when they relate to peers (Uyterhoeven 1989). Regarding our research it is important to highlight the fact that one of its novelties is that it focuses not only on the vertical but also on the lateral relationships of middle managers and investigates their roles and relationships in these directions. Thus the main direction of our research includes these middle managers’ downward vertical and the horizontal lateral relationships.

2.2 Knowledge sharing

Knowledge sharing represents the key knowledge management processes in organizations and is fundamental for generating new ideas and developing new business opportunities (Lin 2007). Huysman and de Wit (2002:23) also stress the significance of knowledge sharing while determining knowledge management, which according to them is „nothing other than managing knowledge sharing”. Géró (2000) emphasises the significance of knowledge sharing besides other activities as well by mentioning that nowadays one of the biggest challenges includes the mapping, using and also the sharing of available knowledge. The reason why knowledge sharing within an organization is so important is defined by Dunford (2000:296) as follows ‘‘much of the key knowledge is held by individuals unless there is some structure to retain it within the organizational memory’’. Furthermore Rodriguez and Edwards (2010: 141) highlights the significance of improving knowledge sharing since it “develops capacities inside the organization”. Finally, the goal of knowledge sharing according to Christensen (2007:37) “can either be to create new knowledge by differently combining existing knowledge or to become better at exploiting existing knowledge”. Regarding the definitions of knowledge sharing, it is mainly described as an activity during which information or other important contents are shared (Bartol, Srivastava 2002; Möller, Svahn 2004; Kocsis 2004; Li 2010). The approach of Bartol and Srivastava (2002) contains information as an element of knowledge sharing and defines it as the action in which relevant information are diffused by employees to others across the organization. Möller and Svahn (2004:220) emphasize that knowledge sharing is “sharing not only codified information, such as production and product specifications, delivery and logistics information, but also management beliefs, images, experiences, and contextualized practices such as business-process development”. Kocsis (2004:41) defines knowledge sharing as “the activity of individuals following their self-interest”. Li (2010:40) also defines knowledge sharing as an activity, but “in which participants are involved in the joint process of contributing, negotiating and utilizing knowledge”. After reviewing these definitions it can be seen that neither do they deal with middle managers and nor they investigate elements that are important regarding the knowledge sharing of middle managers. This has inspired us to create our own definition of knowledge sharing from the combination of the above mentioned ones. Thus our research defines knowledge sharing as a two-way process (giving and receiving knowledge) between the knowledge giver(s) and the knowledge receiver(s) who as participants of knowledge sharing exchange the knowledge found in their minds or the knowledge found in electronic or paper documents furthermore knowledge sharing can occur at the same time when the participants are present or at different times when they make their knowledge explicit.

2.3 Measurement of knowledge management maturity and knowledge sharing

According to Turner and Minonne (2010:167) “in many organisations there is no synchronised approach to measuring the effects of KM practices”. For lack of the synchronised approaches to measure these effects in the following however we make an attempt to review the various measurements of knowledge management maturity and knowledge sharing to reveal those approaches that measure the management of knowledge in organizations.



2.3.1 Measurement of knowledge management maturity

The existing knowledge management maturity models can be categorized into two groups, depending on whether they are based on Software Engineering Institute’s (SEI) Capability Maturity Model (CMM) or not. In the CMM, five levels of maturity are defined and each level is described by a unique set of characteristics. Besides, the technological aspect is more emphasised in these models. Examples of CMM-based knowledge management maturity models are Siemens’ KMMM,Paulzen and Perc’s (2002) Knowledge Process Quality Model. Non-CMM-based knowledge management maturity models contain examples of KPMG’s (2000) Knowledge Journey, Klimko’s (2001) KMMM, TATA Consultancy Services’ 5iKM3 KMMM (Mohanty, Chand 2004), and WisdomSource’s (2004) K3M. These models differ in the number of levels from CMM-based knowledge management maturity models. They describe steps of growth and if they are achieved by the organization then they can reach their knowledge management development (Khatibian et al. 2010). The other two also known types of maturity models are the staged and the continuous maturity models. In staged maturity models the development of a single entity is described by a limited number of maturity levels (usually four to six levels), which are characterised by certain requirements (Paulk et al. 1993). These requirements have to be achieved by the entity in a strict order from the initial level to the final level (Paulk et al. 1993). During development the entity progresses from one level to the next and it cannot omit any level (Paulk et al. 1993). Regarding continuous maturity models the concept of ‘progress area’ is used, where maturity is interpreted in the context of processes and the organization can develop simultaneously in different process areas (Klimko 2001). An example of continuous maturity model is the ‘Knowledge Management Profile’ maturity model which does not require strict ordering either when the knowledge management elements are elaborated and implemented (Gaál et al. 2008). This model shows those areas of knowledge management practice that are outstanding at a given organization and those areas that are lagging behind (Gaál et al. 2011). It can be seen that these models mainly evaluate the maturity levels of organizations and not individuals, thus they cannot be used in our research.

2.3.2 Measurement of knowledge sharing

The majority of studies have measured individual knowledge sharing from the point of view of willingness (or intention) of employees towards knowledge sharing or investigated self-reported knowledge sharing behaviours (Bock et al. 2005; Lin 2007; Jiacheng et al. 2010). In other studies knowledge sharing has been influenced by the organization (Yang, Chen 2007; Bosua, Scheepers 2007; Lin 2008) and thus the organizational perspective has been dominant in the research. While other research has been conducted from the behavioural perspective (Bock et al. 2005; Matzler et al. 2008; Chow, Chan 2008) and knowledge sharing has been influenced by individual behaviour. Since the above mentioned research did not investigate middle managers and their knowledge sharing our research focuses on this field. Regarding middle managers’ knowledge sharing we have considered and investigated the development level of middle managers’ vertical and horizontal relationships. Analysing these relations draws attention to the fact that our research is not an investigation concerning middle managers’ leadership function in which only middle manager-subordinate relationships are examined. Our research investigates the knowledge sharing function and focuses on how mature the function of knowledge sharing is. The development level of this knowledge sharing function is called maturity.

3. Empirical study

3.1 The purpose of the research and the research question

The purpose of our research has been to reveal those components that describe middle managers’ maturity of knowledge sharing who work at medium- and large-sized enterprises in Hungary. Regarding this purpose the following question has been needed to be answered: Question: With what kind of components can middle managers’ maturity of knowledge sharing who work at medium-and large-sized enterprises in Hungary be described?

Zoltán Gaál et al


The elements of middle managers’ maturity of knowledge sharing have been defined and examined by the following categories: availability and the usefulness of knowledge. Availability in our research is measured from the following standpoints:

The investigated middle managers’ availability to other middle managers working on the same organizational level when the investigated middle managers are asked for help,

Other middle managers’ availability working on the same organizational level to the investigated middle managers when the investigated middle managers ask for help;

The investigated middle managers’ availability to their subordinates when the investigated middle managers are asked for help;

The availability of the investigated middle managers’ subordinates to the investigated middle managers when the investigated middle managers ask for help.

The usefulness of knowledge is measured from the following standpoints in our research:

The usefulness of the knowledge given by the investigated middle managers to other middle managers working on the same organizational level;

The usefulness of the knowledge given by other middle managers working on the same organizational level to the investigated middle managers;

The usefulness of the knowledge given by the investigated middle managers to their subordinates;

The usefulness of the knowledge given by the investigated middle managers’ subordinates to the investigated middle managers.

In order to answer the research question the following Hypothesis has been stated: Hypothesis: Middle managers’ maturity of knowledge sharing who work at medium- and large-sized enterprises in Hungary can be characterised by the availability among middle managers, the availability among the middle managers and their subordinates, the usefulness of knowledge among the middle managers, and the usefulness of knowledge among the middle managers and their subordinates. The arrows in Figure 2 represent the elements that are examined regarding this Hypothesis.

Figure 2: Elements of maturity of knowledge sharing under investigation

3.2 Method chosen for testing the hypothesis

The initial assumption regarding the investigated middle managers’ maturity of knowledge sharing was that it could be described by four elements. Furthermore these elements could retain as much of the information of the original variables as possible. Thus principal component analysis (PCA) was selected, since the requirements of retaining large amount of information of the original variables by four components could be tested and proved by the usage of PCA. Another reason of choosing this method was that the principal components were based upon the measured responses (DeCoster 1998). Furthermore as a result of PCA the number of principal components was also less then the number of variables, and this method reduced the number of variables as well (Myatt, Johnson 2009).



3.3 Data collection

The data collection was supported by Department of Management, University of Pannonia between 2007 and 2010. 4000 medium- and large-sized enterprises in Hungary was selected randomly from the average number of 5780 medium- and large-sized enterprises and questionnaires were sent to them by post and via e-mail. The enterprises were asked to have the questionnaire filled in by at least one of their middle managers. The questionnaire of the survey comprised seven categories. One of them was Maturity of Knowledge Sharing which contained questions regarding the extent of availability and usefulness of knowledge based on a 5-point Likert scale. This paper focuses on this topic however other parts of the questionnaire were already published [for example competences found important for knowledge sharing (Szabó, Csepregi 2011)]. The participants of the research can be found in various working areas and industries the data of which are presented in Figure 3.

Figure 3: Distribution of participant of the survey according to industries and working areas

3.4 Results

In this part of the Empirical study the results using PCA will be presented.

3.4.1 Results of KMO and Bartlett’s tests

To determine the appropriateness of the data set for PCA Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity is used. By using correlations and partial correlations for testing whether the variables used are adequate to correlate the KMO statistic is calculated, while Bartlett’s test is used for revealing the relationship between the variables by testing the null hypothesis that the variables are uncorrelated in the population (Hinton et al 2004; Foster et al 2006; Székelyi, Barna 2002). Although the values of KMO statistic can vary from 0 to 1, Kaiser (1974) recommended values greater than 0.5 to be accepted. If the significance value of Bartlett’s test is less than 0.05, then this test is significant and thus the analysis is appropriate (Field 2005; Sajtos, Mitev 2007). The results of both tests can be found in Table 1.

Table 1: The KMO and Bartlett values of maturity of knowledge sharing

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .740

Bartlett's Test of Sphericity Approx. Chi-Square 1105.361

df 28

Sig .000

Table 1 shows that the KMO test with the value of 0.740 has been above the accepted limit of 0.5. In addition, the Bartlett test yields a high Chi-square value of 1105.361, and a significance level of 0.000 which is also under the accepted limit of 0.05. Thus both tests have verified that the data are appropriate for PCA.

Zoltán Gaál et al


3.4.2 Results of Total Variance Explained

The table of Total Variance Explained lists the eigenvalues associated with each component before extraction, after extraction and after rotation. In social science the total cumulative variance explained above 60 % is considered acceptable (Sajtos, Mitev 2007). Table 2 shows the result of Total Variance Explained.

Table 2: Total Variance Explained for maturity of knowledge sharing variables

Total Variance Explained

Component Initial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Total % of

Variance Cumulative

% Total

% of Variance

Cumulative %

Total % of

Variance Cumulative

%

1 3.460 43.252 43.252 3.460 43.252 43.252 1.687 21.085 21.085

2 1.331 16.633 59.885 1.331 16.633 59.885 1.632 20.395 41.481

3 .987 12.341 72.226 .987 12.341 72.226 1.595 19.943 61.424

4 .671 8.387 80.614 .671 8.387 80.614 1.535 19.190 80.614

Extraction Method: Principal Component Analysis The PCA shows that the eigenvalues of the first three principal components have represented up to 61.424 % of the total variance (PC1 21.085%; PC2 20.395%; PC3 19.943%) of the observations. Thus three components would have fulfilled the requirements of exceeding the 60 % limit but it would have been difficult to interpret the components. The percentage of the cumulative eigenvalues has risen up to 80.614% when taking four components into account which thus on the one hand would have fulfilled the aim of our initial assumption on the number of components and on the other hand would have helped the interpretation of the final components. Therefore four components have been retained in the final analysis.

3.4.3 Results of rotated component matrix

Since the interpretation of the Component Matrix is rather difficult the rotation of the components has been needed. By using rotation the output of the PCA is more understandable and the interpretation of the component is much easier. Component loadings are correlation coefficients between the variables and the components and inform about the relationship of the variable and the component. If the variable has a loading value above 0.25 on the component and is loaded only on one component then that variable is considered to belong only to that component. Rotation has two major types: orthogonal rotation (Varimax, Equimax and Quartimax) and oblique rotation (Direct Oblimin, Promax) (Loehlin 1998; Székelyi, Barna 2002; Sajtos, Mitev 2007). Regarding the analysis the use of Varimax rotation method has been chosen, because it finds the angles that can maximize the variance of the squared loadings and it also splits the variables into disjoint sets and thus each variable has been associated with one of the components and this has simplified the interpretation. The results of Rotated Component Matrix can be seen in Table 4.

Table 4: Rotated component matrix of maturity of knowledge sharing

Rotated Component Matrixa

Component

1 2 3 4

Usefulness of other middle managers’ knowledge to middle manager .899 .101 .168 .105

Usefulness of middle manager’s knowledge to other middle managers .823 .018 .156 .274

Availability of subordinates to middle manager .092 .858 .222 .127

Availability of middle manager to subordinates .033 .854 .175 .213

Availability of other middle managers to middle manager .238 .181 .858 .047

Availability of middle manager to other middle managers .104 .240 .833 .222

Usefulness of middle manager’s knowledge to subordinates .094 .209 .124 .874

Usefulness of subordinates’ knowledge to middle manager .340 .146 .132 .756

Extraction Method: Principal Component Analysis.



Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations. The eight variables have been listed in Table 4 in the order of the size of their component loadings. For each variable the strongest (above 0.25) loadings are highlighted indicating which variables load most strongly on which component. As the result of PCA four different components have been identified. Table 5 contains the principal components of maturity of knowledge sharing and the variables that are loaded on them.

Table 5: Components of maturity of knowledge sharing and the variables loaded on them

Name of the Component Name of the Variable

1. Availability among middle managers

other middle mangers’ availability towards the investigated middle managers

the investigated middle managers’ availability towards other middle mangers

2. Availability among

the middle manager and subordinates

availability of the investigated middle mangers’ subordinates towards the middle managers

the investigated middle managers’ availability towards their subordinates

3. Usefulness of knowledge among

middle managers

usefulness of other middle managers’ knowledge to the investigated middle managers

usefulness of the investigated middle managers’ knowledge to other middle managers

4. Usefulness of knowledge among

the middle manager and subordinates

usefulness of knowledge of the investigated middle managers’ subordinates to the investigated middle

managers

usefulness of the investigated middle managers’ knowledge to their subordinates

The first component, the availability among middle managers, includes other middle mangers’ availability towards the investigated middle managers and the investigated middle managers’ availability towards other middle mangers. The availability among the middle managers and their subordinates component contains the availability of the investigated middle mangers’ subordinates towards the middle managers and the investigated middle managers’ availability towards their subordinates. The third component, usefulness of knowledge among middle managers, comprises the usefulness of other middle managers’ knowledge to the investigated middle managers and also the usefulness of the investigated middle managers’ knowledge to other middle managers. The last component, usefulness of knowledge among the middle managers and their subordinates, consists of the usefulness of knowledge of the investigated middle managers’ subordinates to the investigated middle managers and the usefulness of the investigated middle managers’ knowledge to their subordinates. Based on these results the following Thesis can be determined:

Zoltán Gaál et al


Thesis: Middle managers’ maturity of knowledge sharing who work at medium- and large-sized enterprises in Hungary is characterized by the availability among middle managers, the availability among the middle managers and their subordinates, the usefulness of knowledge among middle managers, and the usefulness of knowledge among the middle managers and their subordinates.

3.4.4 Interpretation of the results

The maturity of knowledge sharing is examined by the indices of availability to one another and the usefulness of knowledge, which are presented in the followings. Availability among Middle Managers: The higher the index of availability is the more the investigated middle managers are available to each other, the lower this index is the less the middle managers try to find time for each other. Accordingly the more time the middle manager is willing to find from his work time to help other middle managers, the more the knowledge of these middle managers will enlarge and the more it promotes the growth of the organizational knowledge base as well. Influencing factor for being available originates from the appreciation, understanding and identification with the organizational goals. Middle managers can be more open to be available to other middle managers if their goals and tasks are mutual or if their career depends on the knowledge sharing behaviour. The willingness to be available exposes the sign of cooperativeness within the organization which plays a significant role in these middle managers’ availability to each other. Thus the more they are willing to co-operate, the more they will be available, the less they are willing to co-operate, the lower their availability will be. Those areas in the organization can also be revealed where middle managers rather compete than cooperate that should lead to the revision of personal differences and also the perception and understanding of goals of the given organization. The sign of competition can also draw attention to the “knowledge is power” attitude that can exist within the organization that is against the fulfilment of organizational goals or at least makes it harder to fulfil. Availability among the Middle Managers and their Subordinates: The higher the index of availability is the more the investigated middle manager and his/her subordinates are available to each other, the lower this index is the less they are available for each other. Furthermore the more the middle manager is characterised by having a participative leadership style (Tannenbaum, Schmidt 1958, Hersey, Blanchard 1969) the more they are available to each other, thus the extent of availability shows the extent of participation as well. If the level of availability is higher between the middle manager and his/her subordinates, it results in better communication and the goals for the manager and his/her subordinates can be fulfilled together. By being available to each other the participants can get into win-win situation. Other pairing (win-lose, lose-lose, lose-win) can only lead to low level of availability, which raises the question whether the “knowledge is power” behaviour occurs again. This attitude can cause damage in the communication, and can undermine the fulfilment of organizational and operational goals. In addition problems may also appear if the organizational goals of the manager and the subordinates differ. The higher the power distance of a country is, the more the authority, power differences and status privileges are accepted in that country and the stronger the hierarchical power practices are, and the higher the organizational power distance is, the more the self-interest is dominant within the group (Carl et al. 2004). Thus these features result in a low level of availability among the middle manager and his/her subordinates. Hungary according to Bakacsi and Takács (1998) is characterized by higher power distance and as a result it is understandable that the availability between the middle manager and his/her subordinates is lower. Usefulness of Knowledge among Middle Managers: The higher the index of usefulness of knowledge is, the more valuable the shared knowledge is for the middle managers, the lower this index is the less valuable this knowledge is for the middle managers. Co-operation also plays a significant role in the usefulness of knowledge since the more they are willing to co-operate, the more they will know what kind of knowledge is useful for the others, the less they are willing to co-operate the lower the usefulness of their knowledge will be. If common organizational knowledge, language and jargon emerge in the organization, it can foster the usefulness of knowledge. If middle managers are loyal to their organization they know what kind of knowledge is needed by other middle managers. However if they are not loyal, they will not put effort in sharing useful knowledge with others. When the level of usefulness of knowledge is low, not only the time and effort for sharing but also the intention and the knowledge of the transmitter are queried. The presence of competition leading to the failure of communication can also appear in case of low usefulness. On the other hand, the knowledge that is shared by one middle manager can be misleading since it can be found useful for the transmitter while it is less useful for the recipient(s). The difference in the knowledge base and the existing jargon



can result in knowledge that is less useful for others. By sharing the knowledge which is useful for other middle managers, not only the knowledge base of other middle managers will grow but also the knowledge base of the organization. Usefulness of Knowledge among the Middle Managers and their Subordinates: The higher the index of usefulness of knowledge is, the more valuable the knowledge shared by the parties is for each other, the lower this index is, the less valuable the knowledge shared is. In this case the shared knowledge is in connection with day-to-day work. The low level of usefulness queries not only the competence of the person in that given scope of activities but also the intention of knowledge sharing. Besides the characteristics of the person can also have an affect on how his/her colleagues perceive the quality of the shared knowledge. Middle managers have mainly long term goals, while the subordinates have short term goals which may lead to less usefulness of knowledge for each other. Sharing a part of the needed knowledge can lead to the lack of fulfilment of the tasks. However the usefulness of knowledge probably can be improved by the use of coaching, mentoring, reporting or feedback.

4. Future plans

As a continuation of our research the following options could be taken into consideration:

If our research is carried out among middle managers in a few years changes in the middle managers’ maturity of knowledge sharing could be revealed;

If managers or employees from other levels of the organization are also investigated then their maturity of knowledge sharing could be revealed, and their results could be compared with the recent results of middle managers;

If our research is extended to other countries then the Hungarian results of our research could be compared with the results of other countries considering the national cultural differences as well;

If other parts of the research questionnaire are examined in Hungary or are extended to other countries then the results could also be compared.

By carrying out the research in a few years time the recent results regarding middle managers’ maturity of knowledge sharing could be compared with the “future” results. With this method a change process can be planed, managed and monitored. Investigating employees or managers from other levels of the organization could reveal differences or similarities between these employees and the middle managers regarding maturity of knowledge sharing. Research partners have been found in Bulgaria, Romania and Serbia with the help of whom our research has been extended. As a result of the extension of our research we will be able to compare these countries’ results regarding maturity of knowledge sharing. Since these countries show similarities or differences regarding national culture, the results from these countries should also take into consideration the features and influences of national culture background as well (Heidrich 2002a, 2002b; Szabó et al. 2010). The extended research includes all questions within the questionnaire of the research not only the questions connected to maturity of knowledge sharing, thus the questions and results connected to other parts of the questionnaire can also be compared.

5. Conclusion

This paper has presented the results of an empirical research conducted between 2007 and 2010 among 400 Hungarian medium- and large-sized enterprises. The paper has focused on the research methodology and the results of data analysis. Findings of the research have indicated that four principal components can be considered by middle managers during knowledge sharing. Two of them relate to availability. These are availability among middle managers and availability among the middle managers and their subordinates. The remaining two relate to usefulness of knowledge such as usefulness of knowledge among middle managers and usefulness of knowledge among the middle managers and their subordinates. Concerning availability it has been revealed by Kankanhalli et al. (2005) that knowledge sharing can appear as a result of reciprocation or simply as the enjoyment of helping others. Regarding usefulness of knowledge prior studies have shown that those who are confident in their ability regarding useful knowledge or have higher expertise are more likely to share

Zoltán Gaál et al


their knowledge and are engaged better in knowledge sharing (Cabrera et al. 2006, Constant et al. 1996; Lin 2007). Thus it has been suggested that it is important to increase individuals’ confidence (Wang, Noe 2010).

References

Bakacsi, Gy. and Takács, S. (1998) “Honnan – Hová? A Nemzeti és Szervezeti Kultúra Változásai a Kilencvenes Évek Közepének Magyarországon”, Vezetéstudomány. évf. 29, 2. sz, pp. 15-22

Barnes, J., Bessant, J., Dunne, N. and Morris, M. (2001) “Developing Manufacturing Competitiveness within South African Industry: The Role of Middle Management”, Technovation. Vol 21, No 5, May, pp. 293-309

Bartol, K.M. and Srivastava, A. (2002) “Encouraging Knowledge Sharing: The Role of Organizational Reward Systems”, Journal of Leadership and Organizational Studies, Vol 9, No 1, pp. 64-76

Bock, G.W., Zmud, R.W., Kim, Y.G. and Lee, J.N. (2005) “Behavioral Intention Formation in Knowledge Sharing: Examining the Roles of Extrinsic Motivators, Social-psychological Forces, and Organizational Climate”, MIS Quarterly, Vol 29, No 1, pp. 87-111

Bosua, R. and Scheepers, R. (2007) “Towards a Model to Explain Knowledge Sharing in Complex Organizational Environments”, Knowledge Management Research and Practice, Vol 5, No 2, pp. 93-109

Bower, J.L. (1986) Managing the Resource Allocation Process. Harvard Business School, Boston, pp. 297-298 Cabrera, A., Collins, W.C. and Salgado, J.F. (2006) “Determinants of Individual Engagement in Knowledge

Sharing”, International Journal of Human Resource Management, Vol 17, No 2, pp. 245-264 Carl, D., Gupta, V. and Javidan, M. (2004) Power Distance. In Hous, R.J., Hanges, P.J., Javidan, M., Dorfman,

P.W., Gupta, V. (eds.) Culture, Leadership, and Organizations: the GLOBE Study of 62 Societies, pp. 513-564

Chandler, A.D. (1977) The Visible Hand: The Managerial Revolution in American Business. Harvard University Press, Cambridge, pp. 81-120

Christensen, P.H. (2007) “Knowledge Sharing: Moving Away from the Obsession with Best Practices”, Journal of Knowledge Management, Vol 11, No 1, pp. 36-47

Chow, W.S. and Chan, L.S. (2008) “Social Network, Social Trust and Shared Goals in Organizational Knowledge Sharing”, Information & Management, Vol 45, No 7, pp. 458-465

Constant, D., Sproull, L. and Kiesler, S. (1996) “The kindness of strangers: The usefulness of electronic weak ties for technical advice”, Organization Science, Vol 7, No 2, pp. 119-135

Crouch, A. and Yetton, P. (1988) “Manager-Subordinate Dyads: Relationships among Task and Social Contact, Manager Friendliness and Subordinate Performance in Management Groups”, Organizational Behavior and Human Decision Processes, Vol 41, No 1, pp. 65-82

DeCoster, J. (1998) “Overview of Factor Analysis”, Retrieved from http://www.stat-help.com/factor.pdf Dunford, R. (2000) “Key Challenges in the Search for the Effective Management of Knowledge in Management

Consulting Firms”, Journal of Knowledge Management, Vol 4, No 4, pp. 295-302 Dutton, J.E., Ashford, S.J., O’Neill, R.M., Hayes, E. and Wierba, E.E. (1997) “Reading the Wind: How Middle

Managers Assess the Context for Selling Issues to Top Managers” Strategic Management Journal, Vol 18, No 5, pp. 407-425

Field, A. (2005) Discovering Statistics Using SPSS, Sage, London. Foster, J., Barkus, E., and Yavorsky, C. (2006) Understanding and Using Advanced Statistics. Sage, London. Gaál, Z., Szabó, L., Kovács, Z., Obermayer-Kovács, N. and Csepregi, A. (2008) “Knowledge Management Profile

Maturity Model”, in Proceedings of 9th European Conference on Knowledge Management (ECKM 2008), Southampton Solent University, Southampton, UK, 4-5 September 2008, pp. 209-216

Gaál, Z., Szabó, L., Obermayer-Kovács, N., Kovács, Z., and Csepregi, A. (2011) “Knowledge Management Profile – an innovative approach to map knowledge management practice, In Eardley, A., Uden, L. (eds.) Innovative Knowledge Management: Concepts for Organizational Creativity and Collaborative Design, IGI Global, pp. 253-263

Géró, K. (2000) “Knowledge Management – Múló Háború Avagy a JövQnk?” Könyvtári FigyelQ, évf. 46, 1-2 sz, pp. 104-112

Glasø, L. and Einarsen, S. (2006) “Experienced Affects in Leader–subordinate Relationships”, Scandinavian Journal of Management, Vol 22, No 1, pp. 49-73

Heidrich, B. (2002a) “Business as Unusual: The Role of National Cultural Background in Corporate Life”, KakanienRevisited. Theorie. pp. 1-8. Retrieved from http://www.kakanien.ac.at/beitr/theorie/BHeidrich1.pdf

Heidrich, B. (2002b) “Environmental and Organizational Factors of Mergers and Acquisitions: Acculturation as a Cultural Change Process”, Club of Economics in Miskolc. Vol 1, pp. 9-14

Hersey, P. and Blanchard, K.H. (1969) Management of Organizational Behavior, Englewood Cliffs. NJ: Printice Hall, pp. 107-125

Hinton, P.R., Brownlow, C., McMurray, I., and Cozens, B. (2004) SPSS explained, Routledge, London. Huysman, M. and de Wit, D. (2002) Knowledge Sharing in Practice. Information Science and Knowledge

Management, Kluwer Academic Publishers, Boston. Ireland, R. (1992) “Corporate Culture is Best Conveyed by Mid-level Managers”, Baylor Business Review. Vol 10,

No 1, Spring, pp. 18-19 Jackson, S.E., Chuang, C.H., Harden, E.E., Jiang, Y and Joseph, J.M. (2006) “Toward Developing Human

Resource Management Systems for Knowledge-intensive Teamwork”, Research in Personnel and Human Resources Management. Vol 25, pp. 27-70



Kaiser, H.F. (1974) “An Index of Factorial Simplicity”, Psychometrica., Vol 39, pp. 31-36 Kankanhalli, A., Tan, B.C.Y. and Wei, K.K. (2005) “Contributing Knowledge to Electronic Knowledge

Repositories: An Empirical Investigation”, MIS Quarterly, Vol 29, No 1, pp. 113-143 Kaplan, R.E. (1984) “Trade Routes: The Manager’s Network of Relationships”, Organizational Dynamics, Vol 12,

No 4, pp. 37-52 Khatibian, N., Hasan, T. and Jafari, H.A. (2010) “Measurement of Knowledge Management Maturity Level within

Organizations”, Business Strategy Series, Vol 11, No 1, pp. 54-70, Klimko, G. (2001) “Knowledge Management and Maturity Models: Building Common Understanding”,

Proceedings of the 2nd European Conference on Knowledge Management, pp. 269-278 Kocsis, É. (2004) “A Tudásmegosztás Hatékonysága. Menedzseri Szemlélet Versus Gazdaság-elméleti

Megközelítés”, Társadalom és Gazdaság, évf 26, 1 sz, pp. 39-55 KPMG Hungary (2000) Knowledge Management in Hungary: Research, KPMG Consulting Report, Budapest Kulkarni, U. and Freeze, R. (2004) “Development and Validation of a Knowledge Management Capability

Assessment Model”, Proceedings of the Twenty-Fifth International Conference on Information Systems, pp. 657-670. Jiacheng, W., Lu, L. and Francesco, C.A. (2010) “A Cognitive Model of Intra-organizational Knowledge-sharing

Motivations in the View of Cross-culture”, International Journal of Information Management, Vol 30, No 3, pp. 220-230

Li, W. (2010) “Virtual Knowledge Sharing in a Cross-cultural Context”, Journal of Knowledge Management, Vol 14, No 1, pp. 38-50

Lin, H.F. (2007) “Effects of Extrinsic and Intrinsic Motivation On Employee Knowledge Sharing Intentions”, Journal of Information Science, Vol 33, No 2, pp. 135-149

Lin, W.B. (2008) “The Effect of Knowledge Sharing Model”, Expert Systems with Applications, Vol 34, No 2, pp. 1508-1521

Loehlin, J.C. (1998) Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis, Lawrence Erlbaum, NJ.

Matzler, K., Renzl, B., Müller, J., Herting, S. and Mooradian, T.A. (2008) “Personality Traits and Knowledge Sharing” Journal of Economic Psychology, Vol 29, No 3, pp. 301-313

Mohanty, S.K. and Chand, M. (2004) 5iKM3 Knowledge Management Maturity Model for Assessing and Harnessing the Organizational Ability to Manage Knowledge, TATA Consultancy Services, Retrieved from http://www.tcs.com/NAndI/default1.aspx?Cat_Id=154&DocType=324&docid=419

Möller, K. and Svahn, S. (2004) “Crossing East-West Boundaries: Knowledge Sharing in Intercultural Business Networks”, Industrial Marketing Management, Vol 33, No 3, pp. 219-228

Myatt, G.J. and Johnson, W.P. (2009) “Making Sense of Data II: A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications” Wiley-Blackwell.

Nonaka, I. (1988) “Towards Middle Up/Down Mangement: Accelerating Information Creation”, Sloan Management Review, 29, Spring, pp. 9-18

Pappas, J.M., Flaherty, K.E. and Wooldridge, B. (2003) “Achieving Strategic Consensus in the Hospital Setting: A Middle Management Perspective”, Hospital Topics, Vol 81, No 1, Winter, pp. 15-22

Paulzen, O. and Perc, P. (2002) “A Maturity Model for Quality Improvement in Knowledge Management, Enabling Organisations and Society through Information Systems”, Proceedings of the 13th Australasian Conference on Information Systems, pp 243-253.

Paulk, M.C., Weber, C.V., Curtis, B. and Chrissis M.B. (1993) The Capability Maturity Model: Guidelines for Improving the Software Process, Mellon University, Software Engineering Institute, Reading, MA: Addison-Wesley.

Pommier, M., Shneier, L. and Denning, S. (2000) The Springboard: How Storytelling Ignites Action in Knowledge-Era Organizations, Butterworth Heinemann, London.

Rodriguez, E., and Edwards, J. (2010) “People, Technology, Processes and Risk Knowledge Sharing” Electronic Journal of Knowledge Management, Vol 8, No 1, pp 139-150

Sajtos, L. and Mitev, A. (2007) SPSS Kutatási és Adatelemzési Kézikönyv. Alinea Kiadó, Budapest. Sayles, L.R. (1993) “Doing things Right: A New Imperative for Middle Managers”, Organizational Dynamics, Vol

21, No 4, Spring, pp. 5-14 Schilit, W.K. (1987) “An Examination of the Influence of Middle-level Managers in Formulating and Implementing

Strategic Decisions”, Journal of Management Studies, Vol 24, No 3, pp. 271-293 Schlesinger, L.A. and Oshry, B. (1984) “Quality of Work Life and the Manager: Muddle in the Middle”,

Organizational Dynamics, Vol 13, No 1, pp. 5-19 Siemens (2004). Knowledge management maturity model (KMMM) Retrieved from

http://www.kmmm.org/objects/KMMM_Flyer.pdf Spicer, J. (2005) Making Sense of Multivariate Data Analysis, Sage, California. Staehle, W. and Schirmer, F. (1992) “Lower-level and Middle-level Managers as the Recipients and Actors of

Human Resource Management”, International Studies of Management and Organization, Vol 22, No 1, pp. 67-89

Szabó, L., Kovács, Z., Csepregi, A., Antonova, A. and Jenei, E. (2010) “Is Knowledge Shared the Same Way? A Comparison of Three Central-Eastern European Countries' National Culture and Knowledge Sharing” In Conference Proceedings of the International Knowledge Management in Organizations Conference: Roles and Challenges of Knowledge Management in Innovation for Services and Products. 18th-19th May 2010, Veszprém, Hungary, pp. 21-30

http://www.tcs.com/NAndI/default1.aspx?Cat_Id=154&DocType�

http://www.kmmm.org/objects/KMMM_Flyer.pdf�

Zoltán Gaál et al


Szabó, L., Csepregi, A. (2011) “Competences Found Important for Knowledge Sharing: Investigation of Middle Managers Working at Medium- and Large-sized Enterprises”, The IUP Journal of Knowledge Management. Vol 9, No 3, July, pp. 41-58

Székelyi, M. and Barna, I. (2002) TúlélQkészlet az SPSS-hez: Többváltozós Elemzési Technikákról Társadalomkutatók Számára, Typotex, Budapest.

Tannenbaum, A.S. and Schmidt, W.H. (1958) “How to Choose a Leadership Pattern”, Harvard Business Review. Vol 36, March/April, pp. 95-101

Turner, G. and Minonne, C. (2010) “Measuring the Effects of Knowledge Management Practices” Electronic Journal of Knowledge Management, Vol. 8, No 1, pp. 161-170

Uyterhoeven, H. (1989) “General Managers in the Middle”, Harvard Business Review, Vol 67, No 5, pp. 136-145 Wang, S., Noe, R.A. (2010) “Knowledge sharing: A Review and Directions for Future Research”, Human

Resource Management Review, Vol 20, pp. 115-131 WisdomSource (2004) “K3M Knowledge Management Maturity Model”, Wisdomsource News, Vol 2, No 1,

Retrieved from http://www.wisdomsource.com/contentassets/K3M%20Overview.pdf Xin, K.R. and Pelled, L.H. (2003) “Supervisor–subordinate Conflict and Perceptions of Leadership Behavior: A

Field Study” The Leadership Quarterly, Vol 14, No 1, pp. 25-40 Yang, C. and Chen, L.C. (2007) “Can Organizational Knowledge Capabilities Affect Knowledge Sharing

Behavior?” Journal of Information Science, Vol 33, No 1, pp. 95-109

ISSN 1479-4411 39 ©Academic Publishing International Ltd

Reference this paper as: Haag, M and Duan, Y. “Understanding Personal Knowledge Development in Online Learning Environments: An Instrument for Measuring Externalisation, Combination and Internalisation” The

Electronic Journal of Knowledge Management Volume 10 Issue 1 (pp39-47, available online at www.ejkm.com

Understanding Personal Knowledge Development in Online Learning Environments: An Instrument for Measuring Externalisation, Combination and Internalisation

Markus Haag and Yanqing Duan University of Bedfordshire, Luton, UK [email protected]

1. Introduction

[email protected] Abstract: This paper investigates personal knowledge development in online learning environments using the

perspective of a model adapted from Nonaka and colleagues’ SECI model. To this end, the SECI model, which was originally designed to describe organisational knowledge creation and conversion, was adapted to conceptualise personal knowledge development in online learning at the individual level. As the SECI model was originally conceived at the organisational level, in order to measure personal knowledge development at the individual level in the context of online learning, a measurement instrument was created in order to measure the scores of individual online learners on Externalisation, Combination and Internalisation. It is argued that Socialisation is not a relevant mode in the context of online learning and is therefore not covered in the measurement instrument; this is explained further in the paper. This measurement instrument also examines the interrelationships between the three modes and a new model – the so-called EC-I model – is proposed to depict these interrelationships. The measurement instrument is based on data collected through an online survey, in which online learners report on their experiences of personal knowledge development in online learning environments. In other words, the instrument measures the magnitude of online learners’ Externalisation and Combination activities as well as their level of Internalisation, i.e. the outcomes of their personal knowledge development in online learning. For Externalisation and Combination, formative indicators were used, whereas for Internalisation reflective indicators were used. The measurement instrument is one of the main foci of this paper and is therefore discussed in-depth. In sum, the paper proposes a modified version of the SECI model, extending the applicability of the original SECI model from the organisational to the individual level. It outlines a new measurement instrument which can be used to measure Externalisation and Combination, i.e. personal knowledge development processes, and Internalisation, i.e. personal knowledge development outcomes. Keywords: personal knowledge development, SECI model, EC-I model, measurement instrument, measurement

indicators, online learning

The objective of this paper is to examine and understand personal knowledge development (PKD) in the context of online learning environments (OLEs). It is suggested that one should distinguish between PKD processes and PKD outcomes. For this context, no measurement instrument yet exists that could measure both the processes and the outcomes – the research presented here proposes such an instrument. The research also examines the relationships between PKD processes and PKD outcomes by proposing a new model called EC-I. Knowledge creation at the organisational level has been researched and described intensively by using Nonaka and colleagues’ SECI model (e.g. Nonaka and Takeuchi 1995). Gourlay (2006) claims that SECI has even achieved a paradigmatic status in the field of knowledge management. The model was first proposed in the early 1990s (Nonaka 1991) and has since been modified and extended by, for example, Nonaka (1994), Nonaka and Takeuchi (1995), Nonaka and Konno (1998), Nonaka, Toyama and Konno (2000), Nonaka, Toyama and Byosière (2001), Nonaka and Toyama (2003), Takeuchi and Nonaka (2004), Nonaka, von Krogh and Voelpel (2006), and Nonaka and von Krogh (2009). However, actual measurement instruments and measurement indicators of the SECI model and/or its four modes are extremely rare. Therefore, this paper – which is based on a doctoral research project described in detail in Haag (2010) and in publications related to the research (Haag, Duan and Mathews 2007, 2008, 2009) – presents a measurement instrument for three of the four SECI modes, namely Externalisation, Combination and Internalisation – in the context of online learning. That means that the SECI model will be used as the basis for a new PKD model at the individual level of rather than at the level of organisational knowledge creation for which it has originally been conceived. This new model, called the EC-I model, describes the PKD of an individual learner in





OLEs and extends the applicability of the original SECI model from a focus on the organisational level to a focus on the individual level. First, this paper gives a brief overview of the SECI model and its four modes. Then, the methodology of designing and validating the measurement instrument will be presented. The paper then outlines newly proposed measurement indicators for three of the SECI modes, namely Externalisation, Combination and Internalisation. Finally, the EC-I model, a model of PKD in OLEs, is introduced.

2. The SECI model and its modes

The SECI model describes four modes of knowledge creation through a continuous interaction between explicit and tacit knowledge. The four modes are now explained one by one: Socialisation is defined as the “process of sharing experiences and thereby creating tacit knowledge such as shared mental models and technical skills” (Nonaka and Takeuchi 1995, p 62). In this mode, knowledge is acquired mainly by observation, imitation and learning by doing, similar to an apprenticeship (Nickols 2000). Here, tacit knowledge is converted into tacit knowledge. Externalisation is “typically seen in the process of concept creation and is triggered by dialogue or collective reflection” (Nonaka and Takeuchi 1995, p 64). Here, tacit knowledge is converted into explicit knowledge. Combination “involves combining different bodies of explicit knowledge” (Nonaka & Takeuchi 1995, p 67). This is done by individuals exchanging and combining this knowledge in the form of documents, etc. Here, explicit knowledge is converted into explicit knowledge. This combining and processing of explicit knowledge is likely to lead to more complex and systematic knowledge (Nonaka and Toyama 2003). Finally, Internalisation is the process by which knowledge becomes valuable when it “[knowledge] is internalized in individuals’ tacit knowledge bases through shared mental models or technical know-how” (Nonaka, Toyama and Byosière 2001, p 497), and it is closely related to learning by doing (Nonaka and Takeuchi 1995). Here, explicit knowledge is converted into tacit knowledge. Figure 1 (based on Nonaka and Konno 1998, p 46) depicts the SECI model and its four modes.

Figure 1: The SECI model and its four modes (based on Nonaka and Konno 1998, p 46)

Markus Haag and Yanqing Duan


3. Methodology of designing and validating the measurement instrument

The aim of the research presented here was to design measurement indicators that are valid in the context of high-level, more generic online learning. To this end, a maximally diverse and heterogeneous sample must be recruited; this results in a broad geographical dispersion of the sample. First, the survey was piloted with students and academics at the University of Bedfordshire, UK. Both the content and the wording of the questions were checked and the questionnaire was modified accordingly. Thus, validity issues were addressed (Moser and Kalton 1971), and face validity could be established. In order to get a highly diverse sample, three different ways of accessing participants were followed. This allows for triangulation of data (Denzin and Lincoln 2005) by different types of students (e.g. undergraduates versus postgraduates) and by different modes of instruction (e.g. fully online versus blended learning). It was decided to target a) the students of the eMBA course at the University of Bedfordshire, b) the members of three different Yahoo! Groups, called com-prac, interculturalinsights, and onlinefacilitation, respectively, and c) the members of dialogin The Delta Intercultural Academy, a knowledge community on culture and communication in international business. SurveyMonkey (www.surveymonkey.com) was used to host the survey. The data was then exported into SPSS (Field 2009) and analysed using this statistical analysis software. It is important to note that only some of the members of the three data sources are actual online learners; this means that the response rate could not be calculated. In total, 171 answers could be used in subsequent data analysis. Table 1 shows the shorthand name of the measurement items, the SECI mode they refer to, and the respective questions asked in the survey. The two items for Internalisation shown in square brackets were not used in the final measurement instrument. The reasons for this are discussed in the analysis section below.

Table 1: Measurement items and respective SECI mode and question

Measurement items SECI mode Survey question

Discussion forums

Externalisation (PKD processes)

How often do you post in discussion forums?

Blog How often do you contribute to a blog (e.g. adding, changing or deleting parts of it)?

Wiki How often do you contribute to a wiki (e.g. adding, changing or deleting parts of it)?

Instant Messaging (IM)

How often do you take part in Instant Messaging (IM) with other learners or tutors?

Online chats How often do you take part in online chats with other learners or tutors?

Search engines

Combination (PKD processes)

How often do you use search engines to find materials in addition to those provided by the online learning environment?

Different types of functions

How many different types of functions do you usually access when learning about one particular topic? Examples of these functions, among others, are: discussion forums, blogs, wikis, instant messaging, chats, listening to audio files, watching video files, self-assessment quizzes, downloading course documents, etc.

Getting to know other learners' opinions

How interested are you in getting to know other learners' opinions through reading their postings in discussion forums?

Sharing information How often do you share information with other learners (e.g. posting links or other documents for them to read, using online communication tools to let them know about something, etc.)?

Working together with other learners

How often do you work together with other learners to create new materials (e.g. wikis, blogs, etc.)?

[Application of knowledge] Internalisation

(PKD outcomes)

How strongly do you agree or disagree with the following statements? (same for all five Internalisation items): [I can apply the knowledge that I have acquired in the online learning environment in other contexts.]



Measurement items SECI mode Survey question

[Functions for self-assessment]

[The functions for self-assessment (e.g. quizzes, tests, simulations) help me to learn.]

Acquiring new knowledge

The functions of the online learning environment contribute to me acquiring new knowledge.

Improving my skills The functions of the online learning environment contribute to improving my skills.

I have learned a lot Overall, I have learned a lot through the online learning environment.

In terms of the Socialisation mode, it is suggested here that the direct physical proximity necessary in this mode is, by definition, not possible in an OLE. Nonaka and Toyama (2003) also stress that successful Socialisation is fostered by ‘indwelling’ and ‘living in’ the world, which in turn suggests that the context in which knowledge creation and PKD occurs has to be actively experienced and made sense of. However, in the vast majority of today’s OLEs, particularly at the generic level of online learning, this in-dwelling is normally not possible. Therefore, in the context of this study, Socialisation was considered to be not relevant and was therefore not examined. Moreover, one could argue that some elements of Socialisation are also covered by either Externalisation and/or Combination. The role of Socialisation in online learning requires further clarification and further research. The answer options were identical for all five items representing Externalisation and were based on a Likert-type ordinal scale: ‘Never’ was coded as 1, ‘once or twice a month’ as 2, ‘once or twice a week’ as 3, ‘3-5 times a week’ as 4, and ‘more than 5 times a week’ as 5. The cases that answered ‘Not applicable’ for a particular item were not included in the calculations. The coding for the five Combination items was similar to the Externalisation items, with a coding of 1 to 5 starting from the lowest intensity to the highest intensity. The wording of the five Combination items for the codes of 1 to 5 differs; the respective wordings are:

‘Search engines’: never, rarely, sometimes, often, very often, not applicable

‘Different types of functions’: only one, two, three, four, five or more, not applicable

‘Getting to know other learners’ opinions’: very much interested, somewhat interested, neither interested nor disinterested, somewhat disinterested, not interested at all, not applicable

‘Sharing information with other learners’ and ‘working together with other learners’: never, once or twice a month, once or twice a week, 3-5 times a week, more than 5 times a week, not applicable

The answer options for all five Internalisation items were identical, namely: strongly agree, agree, neither agree nor disagree, disagree, strongly disagree, not applicable.

4. Analysis of measurement indicators for a model depicting personal knowledge development in online learning environments

4.1 Scale development: Formative versus reflective indicators

The nature of the ECI modes in the context of online learning will now be discussed. It is suggested here that constructs of Externalisation and Combination differ from the Internalisation construct in terms of their characteristics of measurement and that one should distinguish between formative and reflective indicators. This distinction will now be discussed. The main approach to the development of measures centres on “scale development, whereby items (i.e., observed variables) composing a scale are perceived as reflective (effect) indicators of an underlying construct (i.e., latent variable)” (Diamantopoulos and Winklhofer 2001, p 269). An alternative to scale development (Hinkin 1995) is the creation of formative or causal indicators and requires the creation of an index rather than a scale (Bollen and Lennox 1991). Formative indicators are observed variables, i.e. items that make up an index, and that cause a latent variable. Contrary to that, reflective indicators, i.e. effect indicators, are observed variables or indicators that are caused by a latent variable (Diamantopoulos and Winklhofer 2001). It is argued here that Externalisation and Combination are latent variables that can be measured by measurement items which are the cause of either the Externalisation or Combination construct. One can say, therefore, that Externalisation and Combination are the dependent variables that are determined by a linear combination of measures of independent variables, namely their respective



formative indicators (Bagozzi 1994). In other words, it is not the objective of the items to represent the same uni-dimensional construct but to give a relevant representation of a range of Externalisation processes or Combination processes, respectively. Contrary to that, it is suggested here that Internalisation should be measured by reflective indicators, because the scale for Internalisation consists of reflective scale items. A more detailed discussion of formative and reflective indicators and their role in this study can be found in Haag (2010).

4.2 Intercorrelations between the measurement items

The intercorrelations between the measurement items for Externalisation, Combination, and Internalisation (ECI items) and their respective aggregates will now be examined. Rather than using the widespread Pearson’s correlation coefficient, Kendall’s tau (k) was used instead. This is because the distribution of the data in this study was considerably non-normal. In such a case, it is often suggested that non-parametric statistics should be used as they do not require normality (Field 2009). In addition to that, it has been suggested that Kendall’s tau is a better estimate of the correlation in the population (Howell 2009).

4.2.1 Intercorrelations between Externalisation items

Table 2 shows the interrelationships, i.e. the correlation coefficients, between the items for Externalisation and the aggregate value for the Externalisation index. All correlations are positive and significant at the p<.001 level (2-tailed), with n=171 as the number of cases. In the tables that follow, one asterisk denotes that the correlation is significant at the 0.05 level (2-tailed), whereas two asterisks denote that the correlation is significant at the 0.01 level (2-tailed).

Table 2: Interrelationships between Externalisation items and their aggregate

Discussion forum Blog Wiki Instant Messaging Online chats Externalisation

Discussion forum – .508** .271** .292** .278** .586**

Blog .508** – .318** .397** .406** .609**

Wiki .271** .318** – .302** .313** .437**

Instant Messaging .292** .397** .302** – .580** .692**

Online chats .278** .406** .313** .580** – .672**

The lowest inter-item correlation is k=.271 for the discussion forum – wiki relationship, whereas the highest is k=.580 for the instant messaging – online chats relationship. The wiki item has the lowest inter-item correlations throughout, suggesting that a wiki is a somewhat distinct feature that stands slightly apart from the other four Externalisation items. However, the wiki item must not be seen as separate from the Externalisation index as the inter-item correlation is still significant. The item-to-total, i.e. item-to-Externalisation aggregate correlation is also very high, ranging from k=.437 for the wiki item to k=.692 for the instant messaging item. Given the very high item-to-aggregate correlations, the chosen items are very likely to represent a similar phenomenon.

4.2.2 Intercorrelations between Combination items

Table 3 shows the interrelationships between the items for Combination and the aggregate value for the Combination index. The significance levels (2-tailed) are also displayed. The spread of the inter-item correlations for the Combination items is larger than for the Externalisation items. Only one was negative, albeit only very marginally, namely the correlation between ‘search engines’ and ‘interest in other learners’ opinions’ with k=-.019. The strongest correlation was found between ‘working together with other learners’ and ‘sharing information with other learners’ with k=.414. All items are significantly positively correlated with the Combination aggregate, with coefficients ranging from k=.309 for ‘search engines’ and k=.624 for ‘types of functions’. It has to be stated again here that all items for both Externalisation and Combination should be kept as indicators for the Externalisation index and Combination index, respectively. This is because the individual items represent separate PKD processes that all add to the aggregate value of either Externalisation or Combination.



4.2.3 Intercorrelations between Internalisation items

Table 4 shows the interrelationships between the items for Internalisation and the aggregate value for the Internalisation index. All correlations are significant at the p<.001 level (2-tailed).

Table 3: Interrelationships between Combination items and their aggregate

Search engines

Types of functions

Interest in other

learners' opinions

Sharing information with other learners

Working together with other learners Combination

Search engines Corr. Coeff.

– .168* -.019 .041 .057 .309**

Sig. . .011 .773 .537 .414 .000

Types of functions Corr. Coeff.

.168* – .230** .321** .277** .624**

Sig. .011 . .000 .000 .000 .000

Interest in other learners' opinions

Corr. Coeff.

-.019 .230** – .149* .215** .429**

Sig. .773 .000 . .022 .001 .000

Sharing information with other learners

Corr. Coeff.

.041 .321** .149* – .414** .550**

Sig. .537 .000 .022 . .000 .000

Working together with other learners

Corr. Coeff.

.057 .277** .215** .414** – .560**

Sig. .414 .000 .001 .000 . .000

Table 4: Interrelationships between Internalisation items and their aggregate

Applying

knowledge Functions for self-

assessment Acquiring new

knowledge Improving

skills

Having learned a

lot Internalisation

Applying knowledge

– .278** .425** .409** .469** .458**

Functions for self-assessment

.278** – .352** .304** .298** .329**

Acquiring new knowledge

.425** .352** – .710** .599** .782**

Improving skills .409** .304** .710** – .662** .828**

Having learned a lot

.469** .298** .599** .662** – .824**

For Internalisation, the inter-item correlations range from k=.278 for ‘applying knowledge’ and ‘functions for self-assessment’ to k=.710 for ‘improving skills’ and ‘acquiring new knowledge’. The item-to-aggregate correlations were also high, ranging from k=.329 to k=.828. It has to be noted that the aggregate for Internalisation is calculated on the basis of taking into account only the following three items: ‘acquiring new knowledge’, ‘improving skills’, and ‘having learned a lot’. This is because the Internalisation scale is regarded as the dependent variable of Externalisation and Combination and a mean scale was used for Internalisation with the aim of improving Cronbach alpha, something which was achieved by deleting two of the items, namely ‘applying knowledge’ and ‘functions for self-assessment’. Thus, Cronbach alpha for Internalisation rose from .823 to .878. On the other hand, as Externalisation and Combination are multidimensional constructs representing conceptually broad definitions rather than overlapping constructs, Cronbach alpha is not a particularly relevant concept (cf. Rojas-Méndez, Davies, Omer, Chetthamrongchai and Madran 2002).



5. The EC-I model: personal knowledge development in online learning environments

It was argued before that Socialisation in its definition by Nonaka and colleagues is not relevant in the context examined in this paper. The proposed model of PKD in online learning therefore includes only three of the SECI modes: Externalisation/Combination – Internalisation (EC-I model). The structural relationships of the model are based on the interrelationships of the ECI modes as shown in Table 5. All correlations are highly significant at the p<.001 level. The strongest correlation is between Externalisation and Combination with k=.533. The effect size of ‘Externalisation as a PKD process’ on ‘Internalisation as a PKD outcome’ is lower than the effect size of ‘Combination as a PKD process’ on ‘Internalisation as a PKD outcome’ (k=.226 versus k=.309). This suggests that Combination processes have a stronger impact on Internalisation, i.e. PKD outcomes, than Externalisation processes have on Internalisation. However, the difference in effect size is not substantial.

Table 5: Interrelationships of the ECI modes: Correlation coefficients

Externalisation Combination Internalisation

Externalisation – .533** .226**

Combination .533** – .309**

Internalisation .226** .309** –

Moreover, the strong correlation between Externalisation and Combination (k=.533) suggests that Externalisation and Combination could be interpreted as the two constituents of one latent factor that shares some characteristics with both Externalisation and Combination. It is argued here that the main shared characteristic is that both modes deal with ‘PKD processes’ as opposed to ‘PKD outcomes’ which are represented by Internalisation. Figure 2 depicts the EC-I model. It has to be pointed out that the EC-I model is only applicable in the context of PKD in online learning and not in other contexts. The model contains the following two main elements: Externalisation and Combination (i.e. PKD processes), and Internalisation (i.e. PKD outcomes). A more detailed discussion of EC-I can be found in Haag (2010).

Figure 2: The EC-I model: A model of PKD in online learning



6. Conclusion

In this paper, a new measurement instrument was discussed which measures the scores of a learner on Externalisation and Combination, representing PKD processes in OLEs, and on Internalisation, representing PKD outcomes in OLEs. This instrument can only be applied in the context of online learning and must be modified to make it suitable and relevant to a different context. Therefore, the items dealing with Externalisation and Combination must be revised in such a way so that they adequately represent the PKD processes of the PKD context under investigation. The measurement items for Internalisation do not necessarily need to be modified because they measure PKD outcomes, a concept that does not differ across PKD contexts. It was also shown that the SECI model can act as a useful starting point to investigate PKD in online learning. A new model, named the EC-I model, was presented in this paper. EC-I is based on the original SECI model and modified in such a way so that it is relevant in the context of PKD in OLEs at the individual level. In order to create further models of PKD in contexts other than online learning, more research is needed to address this shortage of empirical measurement instruments that can measure the magnitude of Socialisation, Externalisation and Combination activities as well as the level of Internalisation, i.e. the end-results of such activities. This will make the SECI model or models based on SECI more useful for both researchers and practitioners in the field of knowledge management.

References

Bagozzi, R. P. (1994) Structural equation models in marketing research: Basic principles, in R. P. Bagozzi (ed) Principles of Marketing Research, pp 317-385, Blackwell, Oxford.

Bollen, K. and Lennox, R. (1991) Conventional wisdom on measurement: A structural equation perspective, Psychological Bulletin, Vol. 110, No. 2, pp 305-314.

Denzin, N. K. and Lincoln, Y. S. (eds) (2005) The SAGE Handbook of Qualitative Research, 3rd

edition, Sage, Thousand Oaks, CA.

Diamantopoulos, A. and Winklhofer, H. M. (2001) Index construction with formative indicators: An alternative to scale development, Journal of Marketing Research, Vol. 38, No. 2, pp 269-277.

Field, A. (2009) Discovering Statistics Using SPSS: (And Sex and Drugs and Rock’n’Roll), 3rd

edition, Sage, London.

Gourlay, S. (2006) Conceptualizing knowledge creation: A critique of Nonaka’s theory, Journal of Management Studies, Vol. 43, No. 7, pp 1415-1436.

Haag, M. (2010) Personal Knowledge Development in Online Learning Environments: A Personal Value Perspective. PhD thesis, University of Bedfordshire, UK.

Haag, M., Duan, Y. and Mathews, B. (2007) Knowledge Development in e-Learning Environments: A Cross-Cultural Perspective, Proceedings of the 8

th European Conference on Knowledge Management (ECKM

2007), Barcelona, Spain, 6th

-7th September 2007, Vol. 1, pp 391-397.

Haag, M., Duan, Y. and Mathews, B. (2008) Trial Application of the Schwartz Value Survey on Personal Knowledge Development through e-Learning. Proceedings of the 9th European Conference on Knowledge Management (ECKM 2008), Southampton, UK, 4th-5th September 2008, Vol. 1, pp 257-266.

Haag, M., Duan, Y. and Mathews, B. (2009) Which Personal Values are Most Relevant to Knowledge Development through E-Learning? Insights from a Delphi Study. Proceedings of the 10th European Conference on Knowledge Management (ECKM 2009), Vicenza, Italy, 3rd-4th September 2009, pp 356-363.

Hinkin, T. R. (1995) A review of scale development practices in the study of organizations, Journal of Management, Vol. 21, No. 5, pp 967-988.

Howell, D. C. (2009) Statistical Methods for Psychology, 7th edition, Wadsworth, Belmont, CA.

Moser, C. A. and Kalton, G. (1971) Survey Methods in Social Investigation, 2nd

edition, Ashgate, Aldershot. Nickols, F. W. (2000) The knowledge in knowledge management, in J. W. Cortada and J. A. Woods (eds) The

Knowledge Management Yearbook 2000-2001, pp 12-21, Butterworth-Heinemann, Boston, MA. Nonaka, I. (1991) The knowledge-creating company, Harvard Business Review, Vol. 69, No. 3, pp 96-104. Nonaka, I. (1994) A dynamic theory of organizational knowledge creation, Organization Science, Vol. 5, No, 1, pp

14-37. Nonaka, I. and Konno, N. (1998) The concept of “ba”: building a foundation for knowledge creation, California

Management Review, Vol. 40, No. 3, pp 40-54. Nonaka, I. and Takeuchi, H. (1995) The Knowledge-Creating Company: How Japanese Companies Create the

Dynamics of Innovation, Oxford University Press, Oxford. Nonaka, I. and Toyama, R. (2003) The knowledge-creating theory revisited: knowledge creation as a

synthesizing process, Knowledge Management Research & Practice, Vol. 1, No. 1, pp 2-10. Nonaka, I., Toyama, R. and Byosière, P. (2001) A theory of organizational knowledge creation: understanding the

dynamic process of creating knowledge, in M. Dierkes, A. Berthoin Antal, J. Child, and I. Nonaka (eds) Handbook of Organizational Learning and Knowledge, pp 491-517, Oxford University Press, Oxford.



Nonaka, I., Toyama, R. and Konno, N. (2000) SECI, ba and leadership: A unified model of dynamic knowledge creation, Long Range Planning, Vol. 33, No. 1, pp 5-34.

Nonaka, I. and von Krogh, G. (2009) Tacit knowledge and knowledge conversion: Controversy and advancement in organizational knowledge creation theory, Organization Science, Vol. 20, No. 3, pp 635-652.

Nonaka, I., von Krogh, G. and Voelpel, S. (2006) Organizational knowledge creation theory: Evolutionary paths and future advances, Organization Studies, Vol. 27, No. 8, pp 1179-1208.

Rojas-Méndez, J. I., Davies, G., Omer, O., Chetthamrongchai, P. and Madran, C. (2002) A time attitude scale for cross cultural research, Journal of Global Marketing, Vol. 15, No. 3/4, pp 117-147.

Takeuchi, H. and Nonaka, I. (eds) (2004) Hitotsubashi on Knowledge Management, John Wiley & Sons, Singapore.

ISSN 1479-4411 48 ©Academic Publishing International Ltd Reference this paper as: Heavin, C and Adam, F. ―Characterising the Knowledge Approach of a Firm: An Investigation of Knowledge Activities in Five Software SMEs‖ The

Electronic Journal of Knowledge Management

Volume 10 Issue 1 (pp48-63, available online at www.ejkm.com

Characterising the Knowledge Approach of a Firm: An Investigation of Knowledge Activities in Five Software SMEs

Ciara Heavin and Frederic Adam Business Information Systems, University College Cork, Ireland [email protected] [email protected] Abstract: An organisation’s ability to successfully compete in a changing market place is contingent on its ability to manage what it knows, in order to serve the objectives of the firm. While it has been argued that due to their size, knowledge management (KM) is not a concern for smaller organisations, in the current economic climate, it is expected that a more formalised approach to KM allows the company to seize opportunities as they arise, and deal with environmental uncertainty more effectively. In view of this, the objective of this study was to devise a classification of knowledge activities (KAs) which facilitates the exploration of a Small to Medium Sized Enterprises (SMEs) in terms of the type and extent to which knowledge is managed. Furthermore, analysis of KAs provided a greater understanding of the fit between the firm’s objectives and the KM approach pursued. In order to achieve this, five case studies were conducted. Based on the classification of KAs identified, a qualitative analysis approach was used to code each of the twenty eight interviews carried out. Both quantitative and qualitative content analysis methods were applied to facilitate data reduction and generate meaning from the significant volume of data collected. The output from this study includes a classification of KAs which provides rich insight into how SMEs are motivated to deal with knowledge as a means of achieving their organisational objectives. From a practitioner viewpoint, this study seeks to offer an improved understanding of a software SMEs’ approach to KM. Keywords: Knowledge Management (KM), Knowledge Activity (KA), knowledge, Small to Medium Sized Software Enterprises (SMES) and software

1. Introduction

While firms understand that managing what they know is important to their success, operationalising such an approach is a more difficult endeavour. This difficulty is attributed to the complex nature of the area where visibly observing, understanding, and measuring an organisation’s approach to managing knowledge remains an arduous endeavour. Edwards (2005, p123) maintains that “evaluation is a crucial topic for both the research and practice of knowledge management, and one that generates much controversy.‖ Recently, Delen and Al-Hawamdeh (2009, p141) argue that with “the massive amounts of information being added to corporate databases and the Internet every day, effective and efficient knowledge discovery has become an imminent problem”. It seems that with more sophisticated technology and the heightened availability of knowledge, KM has become a more pressing issue for those organisations who have not achieved this level of routinisation presented in extant research (Leavitt and Whisler, 1958; Huber, 1984). Moreover, Leavitt and Whisler’s (1958, p41) postulations applied to “the medium and large business firms of the future” consequently overlooking the strategic future of smaller organisations. As the number of SMEs continue to grow through these challenging economic times, where entrepreneurship is increasingly encouraged and where small organisations provide the backbone to many western economies (European Commission, 2007; MacGregor and Bunker, 2000), the importance of understanding how smaller organisations manage what they know has become imperative to their survival. SMEs by their very nature differ from multinational enterprises (MNEs) (Penrose, 1959; Welsh and White, 1981); thus an SME’s KM approach differs to that implemented by larger organisations. From an academic perspective further research in KM and SMEs is imperative, Tan et al. (2009) contend that while testing existing theories in a small firm context has advanced the nature of research in the area, greater attention needs to be attributed to theory building in technology, innovation and corporate social responsibility in small business research. In examining the KM related literature, it may be observed that much of the current literature reflects the discourse proliferated by strategies and technologies implemented in larger organisations. This study seeks to overcome this problem by pursuing a study of KM in SMEs.





2. Defining knowledge

The concept of a continuum in IS is widely considered (Davis and Wetherbe, 1979; Mason and Mitroff, 1973; Davenport and Prusak, 1998; Wurman, 2001). A continuum is defined as a “continuous sequence in which adjacent elements are not perceptibly different from each other, but the extremes are quite distinct.” (Compact Oxford English Dictionary, 2005). Defining data, information and knowledge as distinct and independent phenomena is an arduous endeavour. In particular it is noted that many authors use the terms information and knowledge interchangeably, those (Dennis, Earl, El Sawy, Huber) that considered organisational information processing in the 1970s, 1980s and early 1990s now focus their attentions on KM as an organisational strategy. In essence information processing/management has become, at least in part, knowledge management. Figure 1 represents data, information and knowledge as a continuum.

Figure 1: Knowledge continuum (after Davenport and Prusak, 1998; Wurman, 2001)

Figure 1 illustrates that the extremes of each phenomenon are distinct however there is significant overlap between data/information and information/knowledge. According to Davenport and Prusak (1998, p147) “the distinction between knowledge and information is seen as more of a continuum than a sharp dichotomy. Most projects that focus on internal knowledge [repository] deal with the middle of the continuum-information that represents knowledge to certain users”. The point where information becomes knowledge and vice versa is difficult to pinpoint with complete accuracy, however there is no doubt that these phenomena are closely linked. For the purpose of this study, it is at this point (illustrated in Figure 1) in the continuum where the knowledge focus of this study lies in order to identify and explore the nature of knowledge within the context of an SME.

3. Establishing a classification of KAs

While data and information are defined in static terms, knowledge is defined as having characteristics of movement (Barthelme et al., 1998); “information in action” (O’Dell and Grayson, 1998, p.5). According to Davenport and Prusak (1998, p6) “one of the reasons that we find knowledge valuable is that it is close-and closer than data or information- to action”. From their perspective, “we can use it to make wiser decisions about strategy, competitors, customers, distribution channels, and product and service life cycles” (Davenport and Prusak, 1998, p6). Like many areas of KM, the consideration of actions or activities that result in managing organisational knowledge is well contested amongst researchers. Considering knowledge sharing in the field of Artificial Intelligence, Gruber (1995) advocates the importance of developing ontologies to simplify and abstract a view of the domain under consideration. Holsapple and Joshi (2004) support this view, they acknowledge the need to develop a common language to facilitate the sharing and reuse of knowledge about a domain in order to enable advances in that area of research and, according to Beesley and Cooper (2008), in the business community as a whole. Holsapple and Joshi (2004, p91) refer to knowledge management episode (KME) as “a pattern of activities performed by multiple processors with the objective of meeting some knowledge need”. It is those activities that result in a KME which Holsapple and Joshi (2004) term knowledge manipulation activity or knowledge activity. For the purpose of this research, a definition of knowledge activity (KA) proposed by

Ciara Heavin and Frederic Adam


Kraaijenbrink et al., (2006, p23) is adopted as “transactions or manipulations of knowledge where the knowledge is the object not the result”. It is evident that multiple researchers use different terms for the same/similar activity. This research summarises the terms widely used to describe knowledge activities. These are presented in Table 1, the definitions listed share common verbs such as storing, creating and applying in an organisational context. This research takes a balanced view KAs, discounting the activities proposed by Leonard-Barton (1995) in Table 1, as they have a sole technical focus.

Table 1: The terms widely used to describe knowledge activities

In addition, Nonaka’s (1991) KAs are directly related to knowledge creation and are integrated into a single creation activity. After careful analysis, a refined set of six KAs are presented in Table 2. These encompass the key activities that occur when organisations endeavour to manage what they know. Holsapple and Joshi (2004) state that there is a need for a common ontology that describes knowledge activities clearly and completely. They also advocate the importance of addressing the potential relationship between the activities (Holsapple and Joshi, 2004). The following section deals with each of the knowledge activities presented in Table 2. above using extant literature to cluster the core activities, as well as identifying the links between the KA.



Table 2: An Organisation’s KAs

3.1 Acquire

From Huber’s (1991, p90) perspective “Knowledge acquisition is the process by which knowledge is obtained”. Kraaijenbrink et al. (2006) define knowledge acquisition as knowledge transferred from a source to a company through sub processes: written form, physical objects, people; cooperation between source and recipient; courses; and outsourcing.

3.2 Codify

Knowledge codification converts the generated knowledge into accessible and applicable formats (Davenport and Prusak, 1998). Combine, internalise or absorb are verbs that may be used to describe this activity. Knowledge codification is concerned with the capture, representation and storage of knowledge in computerised knowledge bases (Nevo et al., 2007). Hansen et al.’s (1999) codification strategy supports the use of knowledge repositories e.g. documentation and more specifically technology i.e. databases to store organisational knowledge.



3.3 Store

According to Alavi and Leidner (2001, p127) knowledge storage “involves obtaining the knowledge from organisational members and/or external sources, coding and indexing the knowledge (for later retrieval) and capturing it”. The key to storing organizational knowledge is that the members must be able to anticipate the future needs of the organisation in order for the most valuable knowledge to be stored (Huber, 1991).

3.4 Maintain

The maintenance of knowledge stores is essential to the continued progression of an organisation’s ability to learn. Holsapple and Singh (2004) refer to knowledge control to describe the provision of quantity and quality knowledge as a significant KA. They advocate that knowledge should be accurate, consistent (have validity), relevant and important (have utility). Accordingly, Anderson Consulting (1996) acknowledged the need for control over their knowledge repository to ensure useful, fresh and high quality knowledge, “it would have be more than a dumping ground of documents” (Holsapple and Singh, 2004, p239).

3.5 Transfer

Knowledge transfer is established through person-to-person or system-to-person interaction (Joe and Yoong, 2004). This supports Hansen et al.’s (1999) personalization strategy. From Table 1, it is evident that knowledge transfer occurs inside and outside the organisation. Thus, an organisation may transfer knowledge or receive it from outside the organisation, which is knowledge acquisition.

3.6 Create

Table 1 shows that knowledge creation involves developing new content or replacing existing content within the organization’s tacit and explicit knowledge (Pentland, 1995). While it may be argued that new knowledge may be created through formalized mechanisms e.g. surveys and research and development (Kayworth and Leidner, 2004) others propose that the creation of new knowledge should not be a formalised process but one which is socially constructed and occurs over time through human networks (Brown and Duguid, 2000; Fahey and Prusak, 1998).

4. Research approach

The complex nature of KM coupled with the multifaceted characteristics of small software firms demanded an approach that facilitated the identification and exploration of these phenomena. The researcher pursued the strategy of purposeful sampling as a means of selecting information rich cases for this study (Miles and Huberman, 1994; Patton, 1990); the nature of innovative software products developed by these firms meant that this case displayed a wealth of KAs from the outset. As the objective of this study was to explore the KAs of small software firms, the focus of the study was on the two core business processes of sales and software development. Based on Knoke’s (1994) selection strategy, the positional method was utilised to uncover those key players in the case study. The sales and software development managers were identified; other respondents were selected based on their reputations. In some cases the key technical roles were heavily involved in the sales function and were able to provide an in-depth insight into sales processes at the organisation. Twenty eight respondents in total were interviewed for this study. The ―thick transcripts” (Miles and Huberman, 1994) derived from the interview process coupled with the complexity of KM as an area of research provided the rationale for pursuing qualitative analysis through the use of coding techniques (Ägerfalk and Fitzgerald, 2008). The research approach pursued is synthesised in Figure 2. For the purpose of this study six “seed categories” (Miles and Huberman, 1994) were proposed, these categories were initially derived from the literature. The many classifications of KAs have been assessed and evaluated to develop a complete classification of KAs for the purpose of data analysis in this study. Each KA was assigned a code and this code was utilised to classify the nature of KAs that occur, these categories were then assigned chunks of data derived from the interview transcripts. This iterative process derived a set of categories which capture the occurrences of KAs in the organisation as informed by the interviews. The KAs for each of the firms were collated based on each individual memo generated at the level of the interview. A counting analysis technique was used



to derive meaning from the large volume of data gathered. Miles and Huberman (1994) advocate the use of counting in qualitative analysis “to rapidly see what you have in large batches of data” (p253). The knowledge types were counted, the KAs were counted and the distribution across the six KAs was calculated for each case. The counts were derived from a populated table of KAs for each case (not included); aliases are used to protect the privacy of the companies under consideration.

Figure 2: Schematic overview of analysis process (adapted from Ägerfalk and Fitzgerald, 2008)

5. Findings

This study pursued a replication logic across the five cases. An overview of each organisation is presented for in Table 3.

Table 3: Case background



5.1 Knowledge Activity at HelpRead Ltd

Table 4 identifies 82 instance of KA at HelpRead; the majority of activities presented themselves through knowledge acquisition, storage and transfer. Table 4 identifies the distribution of KAs and the type of knowledge focus for each of these activities - these statistics are derived from the individual tables of KAs. The study identified 82 KAs; however we uncovered 113 instances of knowledge types. The difference in these figures can be explained by single KAs leveraging multiple knowledge types in some instances, thus increasing the instances of knowledge types identified in the analytic memos. It is also important to note that, at the time, HelpRead Ltd. was not in a new product development phase - at a time of new product development, higher number of KAs would be anticipated.

Table 4: Distribution of KAs at HelpRead Ltd.

Using Table 4 the difference in intensity between these types of activities was indicative of HelpRead’s position as a growing organisation. Knowledge acquisition intensity at 21 percent (n=82) showed that HelpRead were focused on building a collective organisational memory that facilitates continued growth through the introduction of new hires and new products. This was particularly important to them in terms of acquiring external knowledge to inform new product development. Fifty three percent of all knowledge acquisition activity was focused on gathering product knowledge supporting Groen’s (2006, p124) view that in high-technology SMEs “intensive interaction is needed to enhance the product creation process with knowledge from external sources”. At 13 percent (n=82) codification activity was relatively low intensity - this was reflective of the uncertainty around what the company needs to know in the future. This is predominantly evident with the Technical FAQ, which lacked buy-in from the entire development team. The Development Manager admitted that as a team “they didn’t know what they should know”. Most codification activity was directly related to refining the discussions at group meetings into documents which are made available over the Intranet. Over 90 percent (n=11) of all codification activity identified in Table 4 was related to product development knowledge. Codification was largely not a sales related activity. The well defined scope of the Goldmine

TM sales system meant that no KA was required to support the refinement and distillation of sales related knowledge. In addition, the experience of the sales team meant they knew what important customer and sales related knowledge should be stored for future use. The high occurrence of storage activities at 29 percent was indicative of the importance placed on storing knowledge in the new Intranet-based quality system - approx 74 percent (17 of n=24 storage activities) of storage activity involved the Intranet. These activities primarily included storing software project documents and employee skills documents, in line with the compliance requirements outlined by IS9001:2000. The codification intensity also included the level of customer information captured and stored by the sales team. This 29 percent reflected the move to store the knowledge gathered



from acquisition, codification and transfer activities. Hoch et al. (1998) purport that software companies with well established structure and processes reduce low level software rework and facilitates software reuse, it appears that HelpRead were moving in this direction. While the company were not focused on very sophisticated process certification, i.e. CMM, leveraging KM approaches offer an effective alternative (Baskerville and Pries-Heje, 1999). Maintenance activities at 10 percent highlighted the company’s focus on maintaining software and product development knowledge. Surprisingly, transfer activity was high intensity at 19 percent - with closer inspection; the role of the Technical Director was integral to this. At 6 percent, knowledge creation was very low. While Table 4 shows that 80 percent of knowledge creation activity was focused on product knowledge, in line with company strategy, the lack of other types of knowledge creation may be attributed to the pressures associated with the rapid growth in employee headcount and the increased product portfolio.

5.2 Knowledge Activity at TravelSoft Ltd

Table 5 shows that 147 instances of KAs were identified at TravelSoft Ltd.

Table 5: Distribution of KAs at TravelSoft Ltd.

The KAs in Table 5 leveraged more than one type of knowledge during a single activity, thus providing the rationale for the 211 instances of knowledge types identified for TravelSoft. The knowledge focus at TravelSoft was quite consistent and reflected the company’s strategic objectives. The emphasis on software development, project, process and product knowledge was clear. Knowledge of the travel industry made up a quarter of the knowledge acquisition activity. At HelpRead Ltd. 82 instances of KAs were observed, KA at TravelSoft was considerably higher at 147 instances. This intensity may be explained by a number of factors. At the time of interview a new Application Solutions Manager had been in place for approximately eight months. From a Telecoms background, he implemented a number of organisational strategies to develop embedded processes and most importantly to bring a new product to the travel software marketplace. It is primarily these management initiatives that contributed to the high density of KAs. Table 5 shows knowledge acquisition activity at 11 percent (16 of n=147), this was due to the acquisition of consultant knowledge on new product development, employee training, relevant books,



journals and travel conferences. In terms of codification at 20 percent activity, project related knowledge was refined and stored. At 21 percent activity, storage activity was almost in line with the volume of codification activity. This indicates that TravelSoft were good at following through on this type of activity. For example the steering committee refine and store the new Adept framework templates in the relevant artefacts. R&D acquisition, codification, storage and maintenance of knowledge contribute to the dense volumes of KA. Activities such as Internet research in the travel area added to the level of knowledge acquisition activities, while refining and storing this knowledge contributed to the volume of codification and storage activity. At 14 percent, maintenance activity was lower than knowledge codification and storage activity. This could have been due to the fact that some of the knowledge stored does not require updating, for example conference and journal papers on the travel industry will not be changed although new papers may be added over time resulting in increased storage activity. Knowledge transfer at 28 percent (41 where n=147) represented the highest volume of KA. Leveraging a variety of routine and non-routine modes (these are outlined in next section). This organisation encouraged knowledge transfer at all levels of the organisation. Knowledge creation is considerably lower at 9 instances (6 percent where n=147). These activities were all generated around new product and process development placing these initiatives at the core of all KAs of TravelSoft at that time. Table 5 shows a spread of 66 percent of KA at TravelSoft across knowledge acquisition, codification, storage and maintenance activity, while transfer and creation activity account for 34 percent of all KA. By comparison, the distribution at HelpRead for the same activities was 73 percent and 27 percent respectively. This shows that through their change process, TravelSoft were good at leveraging the more valuable types of KA.

5.3 Knowledge Activity at Systems Solutions Ltd

Table 6 illustrates a total of 105 KAs identified at Systems Solutions.

Table 6: Distribution of KAs at Systems Solutions

We identified 131 instances of knowledge type across the KAs; this indicates that some KAs leveraged multiple knowledge types. From examining Table 6, it is apparent that knowledge acquisition and maintenance were exceptions in terms of their knowledge focus. Knowledge acquisition was focused on product and customer knowledge, these knowledge types were largely pertinent to the Business Service Management and SAP Solutions divisions focused on software resale. Knowledge maintenance activity was focused on sales knowledge at 38 percent of knowledge focus for that activity. The emphasis on sales primarily reflects the knowledge requirements of these two divisions. From Table 6, the other KAs were focused on software development and project



knowledge serving the knowledge needs of the Business Intelligence (data warehousing) and Application Development divisions. At 105 instances, KA was mostly characterised by its informal nature. The Managing Director admitted that when he is involved with requirements analysis for the business intelligence division the knowledge is documented and stored in an A4 pad. In addition, one Project Manager from the Application Division admitted that it was not uncommon to calculate a project price on the back of a piece of paper in the car park before attending a meeting with a prospective customer. Project related knowledge was codified, stored and maintained in order to meet the requirements of pharmaceutical customers who must abide by Food and Drugs Authority (FDA) regulations. From Table 6, it is evident that at 26 percent, storage activity was higher than both codification and maintenance activity together at 21 percent. This may mean that Systems Solutions store large volumes of documentation without refining and formatting it, and in the longer term, without updating it. As a result, it seems they hold large these volumes for the sole purpose of protecting themselves from external threats such as possible audits. At 15 percent, knowledge acquisition appeared important, however more than half of this activity is attributed to sales and customer interaction. At 5 percent, knowledge creation activity was very low. The Managing Director was the main source of the knowledge creation activity at Systems Solutions. It seems that the time pressures associated with meeting project deadlines means that there was little time for knowledge creation activity amongst the divisions. In the case of Systems Solutions knowledge creation was not the responsibility of those at an operational level. Knowledge acquisition, codification, storage and maintenance account for 66 percent of all KAs while knowledge transfer and creation amount to 34 percent. This is consistent with TravelSoft however, it differs in the case of HelpRead whose focus on knowledge storage activity through the new company Intranet tips the balance of KA distribution towards the earlier activities.

5.4 Knowledge Activity at FinSoft Ltd

Table 7 illustrates 78 instances of KAs identified at FinSoft Ltd..

Table 7: Distribution of KAs at FinSoft Ltd.

We identified 111 instances of knowledge types across the KAs; this indicates that KAs at FinSoft Ltd. leveraged more than one type of knowledge during KAs. Table 7 shows a consistent knowledge focus on software development and project knowledge across knowledge codification, storage, maintenance and transfer activity. Knowledge acquisition activity focused on customer knowledge, while creation activity leveraged product knowledge.

Knowledge acquisition activity is primarily related to the sales and customer knowledge at FinSoft Ltd.. While 11 percent (9 where n=78) of KA was external knowledge acquisition, a large portion of



this activity was impromptu in nature although it was driven by the Head of Sales and the Chief Technology Officer (CTO). Knowledge codification, storage and maintenance activity at 46 percent (43 where n=78) was primarily related to project knowledge and software development activity. However as FinSoft Ltd. is a supplier of investment fund data feeds and bespoke fund management software, this activity was conducted to protect the organisation from issues arising from regular internal audits. Knowledge transfer at 29 percent (23 where n=78) intensity occurred amongst the teams, software developers, quality assurance (QA), data, sales and senior management. While it was admitted that knowledge transfer between the software development team and Quality Assurance was effective, the Head of the Data team disclosed that knowledge transfer amongst the other division needed improvement. Knowledge creation activity was extremely low at 3 instances (4 percent where n=78), management are typically involved in these activities focused around attracting new customers and creating potential new product ideas. However, they did not seem to expend significant resources in creating new ideas in-house. At FinSoft Ltd. the distribution of acquire, codify, store and maintain activities to knowledge transfer and creation activities were at 66 percent and 34 percent respectively. While at HelpRead this distribution was at 74 percent and 26 percent, however the focus there was on the development of the Intranet and collaborative wiki technologies, with knowledge storage activity accounting for a significant proportion of the 74 percent (29 percent storage activity).

5.5 Knowledge Activity at DocMan (Ireland) Ltd

The breakdown of KAs for DocMan (Ireland) Ltd. is presented in Table 8.

Table 8: Distribution of KAs at DocMan (Ireland) Ltd.

From Table 8 at 60 activities, the total volume of KA was low in comparison to the other cases considered in this study. This may be attributed to the nature of the well defined work on software development components at the DocMan site in Ireland. The operations at the Irish site are part of a larger document management software component and the output from DocMan (Ireland) is integrated by the software integrator at the Swiss headquarters. As a result of this task specificity, there appeared to be a set of core KAs from which there was minimal diversification at the Irish site. Table 8 illustrates a significant level of knowledge consistency across all of the KAs. Software development and project knowledge represented at least 57 percent of the knowledge focus for all six KAs. This uniformity across activities also supports the task specialisation activity at the DocMan (Ireland) site. Table 8 indicates that DocMan (Ireland) leveraged some external knowledge resources at 12 percent (7 where n=60) knowledge acquisition, however the main source of knowledge is the headquarters in Switzerland and this was achieved through knowledge transfer activity which is very high at 40



percent (24 where n=60) of total activity. It is from here that the majority of customer requirements are received as well as any new product knowledge. In addition, knowledge creation activity was very low at 3 percent (2 where n=60). This may be the result of the location of this development team with most new ideas being generated at a higher level at company headquarters. Although the total volume of KA was low, the split between the acquire, codify, store and maintain KA at 57 percent and the knowledge transfer and creation activity at 43 percent is more evenly balanced than that observed at HelpRead Ltd., at 73 percent and 27 percent respectively. The geographic location, the task specialisation and the maturity of the parent organisation may be attributed to the knowledge transfer capabilities identified at DocMan Ireland.

6. Discussion

Table 9 synthesizes KAs across the five cases. The highest and the lowest occurrences of KA for each company are coloured green and red respectively. The real number and the percentage of KA instances are presented along with the total KA and percentage of KAs. Table 9 identifies the difference in the number of occurrences of KAs in each company. TravelSoft represents 31 percent (147 where n=472) of all KAs identified. At the time of this study, TravelSoft was very focused on establishing and implementing a project management framework for all new projects as well as an innovation management process for new product development. As a result, it may be that TravelSoft was experiencing exceptionally high levels of KAs at that time. In sharp contrast, DocMan provided the lowest number of instances of KAs with 13 percent (60 where n=472) of all occurrences. While one could attribute this to the number of respondents interviewed for the study (4), it is more likely that this low density of KAs may be explained by the very well defined nature of the work carried out at this small company.

Table 9: KAs in five Software SMEs

In Table 9, it is evident that knowledge transfer at 28 percent (134 n=472) represents the highest intensity of all KAs. For all companies apart from HelpRead Ltd., knowledge transfer was the most intense KA with DocMan displaying the greatest intensity of transfer activity at 40 percent (n=60). This may be due to DocMan’s constant interaction and exchange with their Swiss headquarters, they are



particularly focused on ensuring that software requirements are well understood by all parties coupled with the need to maintain contact with the software integrator, who is responsible for integrating the components developed in the Irish office into the complete software product. Knowledge transfer is undoubtedly high across all companies this may be attributed to the flat structure of SMEs where few employees operate in flexible working environments (Younger, 1990). From Table 9, unlike the other companies the highest incidence of activity occurrence for HelpRead Ltd. is in knowledge storage at 30 percent (24 where n=82). HelpRead’s focus on storage activity was highlighted in this study; it may be explained by their focus on establishing a quality system to meet ISO regulations. Table 9 highlights that knowledge creation activity across all cases was low at 5 percent (234 where n=472). This activity may be low intensity as it is a more difficult phenomenon to observe. However, another reason for the low count of knowledge creation activities could be attributed to the nature of SMEs where it is typical for the manager to be the “driving force” behind the organisation (Nunes et al., 2005) and where the founder has the initial idea and continues to have significant hands on involvement in the running of the organisation (Schein, 1993). In other words, knowledge creation may be the responsibility of a few people in a SME, namely the founder and senior management. Chan and Chao (2008) contend that knowledge generation in SMEs can be improved if management provide more opportunities to software developers in terms of providing opinions and ideas based on their expertise.

7. Knowledge Approach in five SMEs

From this study TravelSoft Ltd. and HelpRead Ltd. can be characterised as knowledge intensive organisations (Davenport and Prusak, 1998; Nonaka, 1995; O’Dell and Grayson, 1998). The considerable number of KAs identified at TravelSoft were the direct result of new product development and process formulation related activity while HelpRead’s customer oriented strategy resulted in significant KA. Both TravelSoft and HelpRead relied heavily on external resources as a means of valuable knowledge acquisition. The emphasis on new product (Groen, 2006) and in the case of TravelSoft new process development characterised the nature of the KM approach for these organisations. Essentially, HelpRead and TravelSoft utilised KAs to support their product development activity (Kraaijenbrink et al., 2006). However, this was done in different ways, depending on the current needs of the company. For example in the case of TravelSoft who did not have a software product but were working towards achieving that goal, the total number of KA was significant. Their focus on software development knowledge, but also on travel industry and process knowledge was substantial and they have implemented collaborative web technology and face-to-face workshops (Daft and Lengel, 1986) to support this goal. In essence, their KM approach achieved their needs in terms of the goals set by the company. The findings at HelpRead supported this, although they were not working through new product development at the time, in which case the total number of KAs might have been higher, their knowledge focus and KAs operated to support new feature and product development activity. It would be expected that instances of such activity and the intensity of the knowledge focus on customer driven product development would ramp up, if HelpRead were to begin a new product development phase. Supporting the view of Davenport and Prusak (1998, p178) who state that “knowledge and learning should always serve the broader aims of the organisation”, depending on their activities and goals HelpRead leverage KM to fit their needs. Systems Solutions had no formalised approach to managing knowledge; however as a high-technology software consulting organisation KAs occurred organically, although some activity was driven by industry regulation. It was evident that apart from software development and project knowledge, Systems Solutions did not focus on niche industry knowledge. In addition, the company relied on readily available company technology such as desktop applications and shared folders to support all KAs. In the same way, FinSoft Ltd did not have a formalised approach or sophisticated information systems in place to support KAs. However, they too were driven by industry regulation to maintain accurate and up to date documentation. While Systems Solutions and FinSoft leveraged knowledge for reasons different to those of HelpRead and TravelSoft, they too pursued KAs that met the needs of the organisation. For both of these organisations, compliance was a key driver of KA. In addition, senior management sales strategy in both companies created a focus on customer and sales knowledge and supporting activities. With the external compliance pressures and an emphasis on generating sales opportunities, KAs were used to support the needs of these organisations.



As a small satellite office to Swiss headquarters, DocMan’s (Ireland) KAs were largely determined by head office. As a result, the nature of their work was highly bound to software component development, while the volume of KA in general was quite low, significant knowledge transfer occurs between the Irish and Swiss office. The company’s extensive reliance on knowledge transfer activities reflected the nature of their business objectives, where a KM approach that serves the goals of the Irish based subsidiary is pursued. The justification for each company’s KA is closely matched to the organisational objectives of that firm. Across the cases there was a common focus on software development, project and product knowledge, although niche knowledge was important in some cases. While the number of KAs varied, the case data showed that across the board, these companies were good at knowledge transfer and evidently, weak at knowledge creation, this is supported by extant Irish research (CSO, 2007).

8. Conclusion

Based on considerable extant research a working definition for knowledge and a classification of KAs was established in order to enable the identification of occurrences of KA across the five cases thus providing an account of how knowledge is managed in each organisation. This classification of KAs provides a common vocabulary which is a useful and practical method of assessing the KM approach of software SMEs in both a tangible and practical manner. This classification could be used by software and other types of SMEs to understand and develop their KAs in order to best serve the needs of the firm. While it has been established that SMEs and MNEs differ (Nissila et al., 2004; Penrose 1959; Wong, 2005), using the vocabulary established in this study, larger organisations could leverage the classification to develop their own approach to KM to serve the function’s or organisation’s activities and goals at a particular time. This study identifies that KM is adapted based on organisational context e.g. to serve the organisation’s business needs as required. In order to leverage KM, an organisation’s KM strategy should support the organisation’s business strategy and objectives. While the importance of exploiting KM to suit an organisation’s strategy is evident from the five cases conducted for this study, additional research should endeavour to understand the rationale for pursuing certain knowledge approaches based on the organisation’s goals or objectives (Carlsson, 2001; Duffy, 1999; Davenport and Prusak, 1998), at a particular time or as a result of environmental change. This study shows that, while the organisational objectives and priorities may be different, KM should be implemented to meet the organisations strategy, in essence developing the appropriate KAs to fit the knowledge needs of the firm, for example, to support new product development activity or to support a focused sales strategy when required. However, if organisational objectives change as a result of environmental uncertainty it would be anticipated that the organisation should realign their KM approach to support new objectives and activities. In effect, an ideal situation requires that an organisation is flexible enough to leverage alternate KAs depending on the businesses needs at a specific time. Essentially, SMEs consider the economy of knowledge when they expend resources to manipulate knowledge in a way that is commensurate to the benefits that stand to be obtained from this effort. This principle has clear implications for further research and practice, as the benefits obtained from a firm’s KM approach must be in keeping with the efforts of its implementation and daily use.

References

Ägerfalk, P. J. and Fitzgerald, B. (2008) Outsourcing to an Unknown Workforce: Exploring Opensourcing as a Global Sourcing Strategy, MIS Quarterly, Vol. 32. No.2, pp385-409.

Alavi, M. (1997) KPMG Peat Marwick U.S.: One Giant Brain, Harvard Business School Case, 9-397-108, Rev. July 11.

Alavi, M. & Leidner, D.E. (2001) Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues, MIS Quarterly, 25(1), pp. 107-136.

Arthur Anderson and American Productivity and Quality Center. (1996). The Knowledge Management Tool: External Benchmarking Version, Winter.

Baskerville, R. and Pries-Heje, J. (1999) ―Managing Knowledge Capability and Maturity‖, Databases for advances in Information Systems, Vol. 20, No. 2, pp. 26-43.

Barthelme, F., Erime, J.L. and Rosenthal-Sabroux, C. (1998) ―An architecture for knowledge evolution in organisations‖, European Journal of Operational Research, Vol. 109, No. 2, pp. 414-427.

Beesley, L. and Cooper, C. (2008) ―Defining knowledge management (KM) activities: towards consensus‖, Journal of Knowledge Management, Vol. 12, No. 3, pp. 48-62.

Brown, S.J. and Duguid, P. (2000) ―Balancing Act: How To Capture Knowledge Without Killing It‖.Harvard Business Review, May- June, pp73-80.



Carlsson, S. A. (2001) ―Knowledge Management in Network Contexts‖, Proceedings of the 9th

European Conference on Information Systems, Bled, Slovenia, pp. 616-627.

Cecez-Kecmanovic, D. (2000) “Understanding Knowledge Sharing in Organizational Decision Making Supported by CMC”, IFIP TC8/WG8.3 International Conference on Decision Support through Knowledge Management, 9-11 July 2000, Stockholm, Sweden, pp. 77-90.

Chan, I. and Chao, C.K. (2008) Knowledge Management in Small and Medium Sized Enterprises, Communications of the ACM, 51, 4, pp83-88

Central Statistics Office (CSO) (2007) ―Small Business in Ireland‖ http://www.cso.ie/releasespublications/documents/other_releases/smallbusiness.pdf (Last Accessed June 16th 2009)

Choo, C. (1996) ―An Integrated Information Model of the Organization: The Knowing Organization‖, URL=http://www.fis.utoronto.ca/people/faculty/choo/FIS/KO/KO.html1 contents. (Last accessed March 18TH 2008)

Daft, R. L. and Lengel, R.H. (1986) „Organizational information requirements, media richness and structural design’, Management Science, Vol. 32, No. 5, pp. 554-571.

Davenport, T.H., and Prusak, L. (1998) Working Knowledge. How Organizations Manage What They Know. Boston, Mass.: Harvard Business School Press, 1998. Transaction Engineering Management, 23, pp163-167.

Davis, C. K. and Wetherbe, J.C. (1979) ―An Analysis of the Impact of Distributed Data Processing on Organizations in the 1980s‖, MIS Quarterly, Vol. 3. No. 4, pp. 47-56.

Delen, D. and Al-Hawamdeh, S. (2009) ―A holistic Framework for Knowledge Discovery and Management‖, Communications of the ACM, Vol. 52, No. 6, pp. 141-145.

Duffy, N.M. (1999) ―Benchmarking Knowledge Strategy‖, In N.Duffy, A. Jooste & L. Whittaker (Eds.), Leveraging Knowledge for Business Performance, Wits Business School, Johannesburg, pp. 12-24.

Edwards, J. (2005) ―Editorial‖, Knowledge Management Research & Practice 3, pp.123-124. European Commission (2007) ―Small and medium-sized enterprises - Key for delivering more growth and jobs. A

mid-term review of Modern SME Policy" http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:52007DC0592:EN:NOT (Last accessed June 17th 2009)

Fahey, L. and Prusak, L. ―The Eleven Deadliest Sins of Knowledge Management‖, California Management Review, 9, pp449-459.

Groen, A. (2006) ―Assessment – Making Sense of it All‖, Knowledge Integration: The Practice of Knowledge Management in Small and Medium Sized Enterprises, Jetter, A, Kraaijenbrink, J. and Wijnhoven, F. (eds), pp.115-126.

Gruber, T. R. (1995) ―Toward Principles for the Design of Ontologies Used for Knowledge Sharing‖, International Journal Human-Computer Studies, Vol. 43, No. 5-6, pp. 907-928.

Hansen, M., Nohria, N and Tierney, T. (1999): What’s Your Strategy for Managing Knowledge? Harvard Business Review, March-April, 106-116.

Holsapple, C.W. and Whinston, T. (1987) Knowledge-based orgnanizations, The Information Society, 2, pp77-90.

Holsapple, C. and Singh, M. (2004) The Knowledge Chain Model: Activities for Competitiveness, Handbook on Knowledge Management, Eds Holsapple, C.W. ,Verlanger: Berlin.

Holsapple, C. and Joshi, K. (2004) A Knowledge Management Ontology, Handbook on Knowledge Management, Eds Holsapple, C.W. ,Verlanger: Berlin.

Huber, G.P. (1984) ―The Nature and Design of Post Industrial Organisations‖, Management Science, Vol. 30, No. 8, pp. 928-951.

Huber, G.P. (1990). A theory of the effects of advanced information technologies on organizational design, intelligence, and decision making, Academic Management Review, 15(1), pp47-71.

Huber, G.P. (1991), Organisational Learning: The contributing Processes and the Literatures, Organisation Science, Vol. 2, No.1, pp88-115.

Joe, C. and Yoong, P. (2004) ―Harnessing the Knowledge Assets of Older Workers: A Work in Progress Report‖, in Proceedings of the 2004 DSS Conference, Prato, Italy.

Kayworth, T. and Leidner, D. (2004) Organizational Culture as a Knowledge Resource, Handbook of Knowledge Management 1, Eds Holsapple C.W., Verlanger: Berlin.

Knoke, D. (1994) Networks of elite structure and decision making, in Advances in Network Analysis: Research in the Social and Behavioural Sciences, eds. S. Wasserman and J.Galaskiewicz, Sage, Thousand Oaks, CA.

Kraaijenbrink, J., Faran, D. and Hauptman, A. (2006).Knowledge Integration by SMEs – Framework, Knowledge Integration: The Practice of Knowledge Management in Small to Medium Sized Enterprises. Eds Jetter, A., Kraaijenbrink, J. Schroder, H., Wijnhoven, F.,Springer.

Leavitt, H.J. and Whisler, T.L. (1958) ―Management in the 1980s‖, Harvard Business Review, November-December, pp. 41-48.

Leonard-Barton, D. (1995) Wellsprings of knowledge: building and sustaining the sources of innovation, Boston , MA: Harvard Business School Press.

Mason, R.O. and Mitroff, I.I. (1973) ―A program for research on management information systems‖, Management Science, 19, pp. 475-485.

http://www.cso.ie/releasespublications/documents/other_releases/smallbusiness.pdf

http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:52007DC0592:EN:NOT

http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:52007DC0592:EN:NOT

http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:52007DC0592:EN:NOT



MacGregor, D.J. and Bunker, R.C. (2000) ―EDI and Small//Medium Enterprises‖, Electronic Commerce Opportunities and Challenges, Rahman, S.M. and Raisinghani, M.S. (eds), Idea Group Publishing, Hershey, pp. 142-151.

Miles, M.B and Huberman, A.M. (1994) Qualitative Data Analysis, Sage Publications. Nevo, S., Wade, M.R., Cook, W.D. (2007). An examination of the trade off between internal and external IT

capabilities, Journal of Strategic Information Systems, 16, pp5-23. Nissila, J., Lauri, A.T., Suoimmi, R. and Puhakainen, J. (2004) ―The House That Jack Built: eBusiness Models for

SMEs‖, 17th

Bled eCommerce Conference. Nonaka, I. (1991) ―The Knowledge Creating Company‖, Harvard Business Review, November-December, Vol.

69, pp. 96-104. Nonaka, I. and Takeuchi, H. (1995) The Knowledge-Creating Company. Oxford University Press, New York, NY. Nunes, M. G., Ananasingh, F., Eaglestone, B.and Wakefield, R.(2005) Manager’s Perceptions of the Value of

Knowledge Management In Small to Medium Sized Knowledge-Intensive Enterprise (SMEs), Journal of Knowledge Management Practice, December.

Oxford English Dictionary of Current English (Compact) (2005), Third Edition ISBN-13: 978-0-19-861022-9. Patton, M. Q. (1990) Qualitative Evaluation and Research Methods, Sage Publications, Thousand Oaks,

California. Penrose, E. (1959) The Theory of the Growth of the Firm, Oxford, Oxford University Press. Pentland, B. (1995). ―Information systems and organizational learning: the social epistemology of organizational

knowledge systems‖. Accounting, Management and Information Technologies, Vol. 5 No. 1, pp1-21. Schein, E.H. (1993) ―On Dialogue, Culture, and Organizational Learning‖, Organizational Dynamics, Fall, Vol. 22,

No. 2, pp. 40-51. Sparrow, J. (2000). Knowledge Features in Small Firms. Operations Research Society KMAC Conference.

University of Aston, 17-18 July. Szulanski, G. (1994) Intra-Firm Transfer of Best Practices Project. American Productivity and Quality Center.

Houston, Texas. Tan, J., Fischer, E., Mitchell, R. and Phan, P. (2009) ―At the Center of the Action: Innovation and Technology

Strategy Research in the Small Business Setting‖, Journal of Small Business Management, Vol. 47, No. 3, pp. 233-262.

Van Der Spek, R. and Spijkervet, A. (1997) "A knowledge management: dealing intelligently with knowledge", in Liebowitz, J., Wilcox, L. (Eds), Knowledge Management and its Integrative Elements, CRC Press, Boca Raton, FL, pp. 31-59.

Welsh, J. and White, J. (1981) ―A small business is not a little big business‖, Harvard Business. Review, July-August, pp.18-32.

Wiig, K.M. (1993) Knowledge Management Foundations – Thinking About Thinking – How People and Organizations Create, Represent, and Use Knowledge. Schema Press, Arlington, TX.

Wong, K, Y. (2005) ―Critical success factors for implementing knowledge management in small and medium enterprises‖, Industrial Management and Data Systems, Vol. 105, No. 3, pp. 261-279.

Wurman, R. S. (2001) Information Anxiety 2. Que, Indiana, USA. Younger A (1990) ―Q-share--a share of TQM‖, TQM Magazine. October, pp. 275-278.

ISSN 1479-4411 64 ©Academic Publishing International Ltd Reference this paper as: Maracine, V, Iandoli, L, Scarlat, E and Nica, A, S. ―Knowledge use and Sharing into a Medical Community of Practice; the Role of Virtual Agents (Knowbots)‖ The

Electronic Journal of Knowledge

Management Volume 10 Issue 1 (pp64-81, available online at www.ejkm.com

Knowledge use and Sharing into a Medical Community of Practice; the Role of Virtual Agents (Knowbots)

Virginia Maracine1, Luca Iandoli2, Emil Scarlat1 and Adriana Sarah Nica3 1Department of Economic Cybernetics, Faculty of Economic Cybernetics, Statistics and Informatics, Bucharest University of Economics, Romania 2Department of Business and Managerial Engineering, Faculty of Engineering, University Federico II, Naples, Italy 3Clinical Rehabilitation Department of National Institute of Rehabilitation and Physical Medicine, University of Medicine and Pharmacology Carol Davila Bucharest, Romania [email protected] [email protected] [email protected] [email protected] Abstract: Knowledge-oriented organizations are bricks for the knowledge-based society construction. Building knowledge-based society and economy suppose challenging transition processes from the classical structure of an organization to new organizational forms that help to fill the gap between actual society and the future knowledge-based society and economy. This transition generates new issues in knowledge creation and sharing processes, related to the particularities of the new organizational forms. Therefore, in the last few years, our researches are oriented to developing and testing a number of forms of organization designed to facilitate an efficient and effective transition toward the knowledge-based society, like communities of practice, (virtual) networks of professionals or knowledge ecosystems (KE). Under this general frame, this paper presents the results of our research aiming to capture the necessary changes that a medical organization specialized in rehabilitation (the National Institute of Rehabilitation and Physical Medicine from Bucharest, Romania - INRMFB) has to undertake for converting its classical structure into a new knowledge-oriented one, possible and easily to being integrated into a Virtual Network for Home Health Rehabilitation of the impaired people – the meta goal of our research in recent years. Specifically, within its five sections, the paper outlines: 1. An introduction in the macro and micro-level empirical setting in which the study is carried out; 2. The methodological approach based on Social Network Analysis (SNA). Although quit often used in the medical field, as we will see in the second section of the paper, the SNA methods and models aren’t used yet in the particular area of health rehabilitation; 3. The objectives of the empirical study that can be summarized as follows: Mapping of the knowledge flows & needs in the target community of practice. The aim of this step is to produce an accurate picture of the knowledge flows that the target community identified at the INRMFB actually enacts in the accomplishment of its organizational objectives. Analysis & Diagnosis: Identification of critical aspects and areas of improvements (e.g. knowledge needs, knowledge bottlenecks, structural determinants of inefficiency or of poor performance). Design: definition of the functional specifications for redesigning the agents, network and of the functionalities of Knowbots. 4. The survey we have designed for data collection. According with the particularities of the macro and micro-level in which our study is carried out, we have designed a survey that will help us both for diagnosing the knowledge-sharing-structure of INRMFB, and for finding adequate solutions for potential critical aspects identified in this medical facility.; 5. A set of conclusions and recommendations for the new knowledge-oriented organizational structure to be created within the INRMFB. Alongside with performing SNA in the health rehabilitation field, an important output of our study is to find answer to the following questions: Cans the classical organizational structure of the INRMFB be transformed into a knowledge-based one, by reengineering the knowledge flows and agent’s roles? If and where within the actual structure a virtual knowledge agent (knowbot) can and should be integrated? Our paper is a consequent continuation of our work in the KE area, contributing to the completion of an integrate vision over the role of the KM techniques, human and virtual agents in the emerging of knowledge-based society. It presents a work still in progress, the final results of our study going to be presented within the ECKM2011 conference. Keywords: community of practice, healthcare knowledge ecosystems, social network analysis, knowledge agent (Knowbot), collective learning, knowledge-based organization.

1. Macro and micro environment of our study

For achieving our research goals – discovering, developing and testing new forms of organizations that help to fill the gap between actual society and the future knowledge-based society and economy (communities of practice (CoP), networks of professionals or KEs.), by collaborating with our colleagues from INRMFB (medical staff, professors, and researchers) we have designed a study that consists in:







Analyzing the macro and micro-level empirical setting in which the study is carried out;

Designing the main objectives of the empirical study and the methodological approach;

Creating a personalized survey for data collection;

Collect and process the necessary data;

Drawing conclusions and recommendations both for the old and the new knowledge-oriented organizational structure to be created within the INRMFB.

Some of these phases were already accomplished, while others are running as we speak. Both of them are described in our paperwork.

1.1 INRMFB macro-level empirical settings

For having a complete image over the extent, links, interdependencies and influences, we start our paper by analyzing both the macro and micro environment that define the activity of INRMFB. Tacking account that the Healthcare Ecosystems are among the most complex networks (M<r<cine, Scarlat, 2008), identifying all the ―agents‖ and the interdependencies within such a structure is crucial for a good diagnostic of the present knowledge transfer network, and especially for finding the best solutions for increasing the fluidity of the knowledge creation and sharing processes. A graphic overview of the macro-environment that includes the INRMFB is comprised by figure 1. As we can see in figure 1, the INRMFB is part of a true Healthcare Ecosystem (M<r<cine, Scarlat, 2010). The typology of agents involved in the current process at macro level is quite wide, including hospitals (especially those having emergency rooms), medical products suppliers, Laboratories, Pharmacies, Universities, national authorities for scientific research, Romanian Healthcare Ministry, and also links with knowledge repositories accessible through the Internet. The existence of such a diversity of agents, links and knowledge flows brings many particularities in the process of knowledge creation and sharing. Also, an increasing variety of competencies – other than the medical ones – are more and more a must for the members of a medical community of practice:

Extensive usage of the computers and dedicate software;

Virtual communications with colleagues and medical authorities;

Continue learning on how to use the advanced technologies embedded with the new equipments;

Facing the new attitude of the patients that are more and more informed and educated (also by the Internet) on their medical conditions.

1.2 INRMFB micro-level empirical settings

Going deeper and looking at the micro-level, the structure of the INRMFB also involves different typologies of agents (figure 2). The main clusters we have found here are: a) The Medical Community of Practice – Medical staff. For the Rehabilitation Clinic no. 3 (the one in which we have started our study), this CoP includes 36 employees (out of all the 38 existent positions within this clinic):

4 doctors specialists in medical recovery, one of them is the chief of the clinic;

9 professional nurses – 6 of them were graduated post high school medical courses (2 years of study) and the other 3 were graduated the University of Medicine on the Bologna system;

10 physiotherapists – 6 of them are physiotherapist nurses and they were graduated post high school medical courses (3 years of study) and the other 4 were graduated the University of Medicine (4 or 5 years of study) and they are professional physiotherapists and kineto-therapists;

2 masseurs ;

1 psychologist also specialist in speaking deficiencies;

1 instructor of medical gym;

1 social worker – this position is vacant, in the Romanian medical educational system there are no programs that could train specialists on this domain;

Virginia Maracine et al


1 occupational therapist – this position is also vacant;

8 nursing aids;

1 statistician and 1 department secretary.

Healthcare Ministry

Laboratories,

Pharmacies

Medical products suppliers,

Prostheses and Orthoses

suppliers

National Institute of Rehabilitation, Physical Medicine

and Balneo-climatology from Bucharest (INRMFB)

External

database

Medical Ontology

INTERNET

Bucharest

University of medicine

National Authority for Scientific Research

Emergency

Hospitals

Other Romanian and foreign

Universities of medicine

Medical Rehabilitation

Clinic 1


Clinic 2

Medical

Rehabilitation Clinic 4


Clinic 3

Figure 1: Health rehabilitation within the INRMFB – macro level

b) The “Patient” CoP – i.e. the disabled patient, his/her family, friends, and colleagues. Within the Rehabilitation Clinic no 3, in average there are 60 patients, usually each of them being hospitalized for 2 or 3 weeks according with the severity of the impairment. In the present, in Romania, the demand for medical services in the rehabilitation area far exceeds the hospitals’ offer. Therefore, in order to increase the ratio offer/demand for rehabilitation services, the hospital’s management team succeeded to find some alternative solutions for the people that need specialized medical assistance, but who can be hospitalized only if they are willing (or if they can) to wait between 2.5 and 3 months. Such alternative solutions for the INRMFB consist in programs such as:

The “Outpatient” program – the patient come into the hospital just to receive the treatment, without being hospitalized; and

The “Hospital by day patient” program – the patient is hospitalized only during the day and is supervised to accomplish his rehabilitation program, without receiving any medication or food from the hospital.

In this way the number of assisted patients within the hospital was increased with around 30% per year. In this moment, from the structure pictured in figure 2, within all four Rehabilitation Clinics from the INRMFB, the Occupational therapist and the Social Worker are missing agents. This is in fact one of the major lacks of the Romanian healthcare system – the professionals like social workers, occupational therapists, and psychologists.



–

PATIENT

Social workers

Occupational

therapists

Medical staff

Medical Rehabilitation Clinic

1, 2, 3 or 4

Medical staff

Medical staff

Figure 2: The actual classic structure of a rehabilitation clinic of the INRMFB

In the future, by extending the study to the “Patient” CoP we intent to find out how their recovery process is affected by the missing of the Occupational therapists, Social Workers and Knowbots.

2. Objectives of the study and the methodological approach

The objectives we intent to achieve through our study can be summarized as follows:

Mapping of the knowledge flows & needs in the target medical community of practice at the INRMFB;

Analysis & Diagnosis: Identification of critical aspects and areas of improvements (e.g. knowledge needs, knowledge bottlenecks, structural determinants of inefficiency or of poor performance);

Design: definition of the functional specifications for redesigning the agents, network and of the functionalities of Knowbots.

These objectives will be achieved using the Social Network Analysis (SNA) specific methodology. That is way we start this section of the paper with a brief of the SNA methods and tools, and we continue it with the detailed analysis of each of the objectives of our study above outlined.

2.1 SNA – specific methods, tools, software and applications

2.1.1 SNA – the concept, models and methods

As we all know, a social network is a very complex social structure made of nodes (individuals – human or virtual – or organizations) that are tied by one or more specific types of interdependency (values, ideas, financial exchange, information and knowledge, friendship, kinship, conflict or trade). SNA views social reality in terms of nodes – individual agents within the networks –, and ties – the relationships between the agents. The social networks operate on many levels, from families up to the level of nations or group of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals.



SNA is an analytic approach, with its own theoretical statements, methods, models, software, researchers and applications. SNA produces a lot of models and methods, where the attributes of individuals are less important than their relationships and ties with other actors within the network. Social networks models (especially the dynamic models of networks) have also been used to examine how organizations interact with each other, characterizing the many informal connections that link executives together, as well as the evolution of associations and connections between individual employees at different organizations. SNA methods also play a key role in hiring, in business success, and in job performance. The models and methods used in SNA are based on a set of networks metrics such as:

Betweenness – degree an individual lies between other individuals in the network; it's the number of people who a person is connecting indirectly through their direct links;

Closeness – the degree an individual is near all other individuals in a network (directly or indirectly). It is the inverse of the sum of the shortest distances between each individual and every other person in the network;

Centrality – the count of the number of ties to other actors in the network;

Flow betweenness centrality – the degree that a node contributes to sum of maximum flow between all pairs of nodes (not that node);

Eigenvector centrality – a measure of the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node in question.

Centralization – the difference between the number of links for each node divided by maximum possible sum of differences;

Clustering coefficient – a measure of the likelihood that two associates of a node are also associates themselves. A higher clustering coefficient indicates a greater ―cliquishness‖;

Cohesion – the degree to which actors are connected directly to each other by cohesive bonds;

Radiality – degree an individual’s network reaches out into the network and provides novel information and influence;

Reach – the degree any member of a network can reach other members of the network.

Structural cohesion – the minimum number of members who, if removed from a group, would disconnect the group;

Structural equivalence – the extent to which actors have a common set of linkages to other actors in the system. The actors don’t need to have any ties to each other to be structurally equivalent.

Most of these metrics are of our interest in analyzing the dynamics of the CoPs within the INRMFB’s rehabilitation clinics.

2.1.2 SNA software

Due to the extraordinary dynamics of the economic and social areas where SNA methods are used today, a wide range of SNA software was developed, like for example:

Detica NetReveal – Social Network Analysis for insurance or banking fraud, crime detection, intelligence, tax evasion, border control and network risk based targeting (http://www.deticanetreveal.com/);

Indiro SNA Plus – Highly scalable Social Network Analysis for Telecoms (http://www.idiro.com/);

Keyplayer - a very good program for identifying nodes whose elimination can disrupt a network (http://www.analytictech.com/keyplayer.htm);

MetaSight – Email/communication network visualization and analysis (http://www.morphix.com/index.htm);

NodeXL – Network Overview, Discovery and Exploration for Excel (http://nodexl.codeplex.com/);

ORA – Social Network Analysis, Network Visualization, Meta-Network Analysis, Trail Analysis, Geospatial Network Analysis, Network Generation (http://www.casos.cs.cmu.edu/projects/ora/index.html);

http://www.deticanetreveal.com/

http://www.idiro.com/

http://www.analytictech.com/keyplayer.htm

http://www.morphix.com/index.htm

http://nodexl.codeplex.com/

http://www.casos.cs.cmu.edu/projects/ora/index.html



StOCNET – open software system for the statistical analysis of social networks using advanced statistical methods based on explicit probability models for dynamic networks (http://stat.gamma.rug.nl/stocnet/);

UCINET 6 – excellent for general SNA, having good help menus (http://www.analytictech.com/ucinet.htm).

Of interest for our study is Microsoft’s NodeXL – an open-source template for using with Excel that goes further than other SNA tools and provides instant graphical representation of relationships of complex networked data. NodeXL was developed by a multidisciplinary team of experts and it is of interest also to researchers and students studying visual and network analytics and their application in the real world. Networks can be imported from and exported to a variety of file formats, and built-in connections for getting networks from Twitter, Facebook, YouTube, and a local email are provided.

2.1.3 SNA medical applications

As applications, until now, SNA and network modelling approaches have been used in epidemiology to help understand how patterns of human contact aid or inhibit the spread of diseases such as HIV in a population. The evolution of health care networks can sometimes be modelled by the use of agent based models, providing insight into the interplay between communication rules, physicians’ opinions and medical infra-structure. Diffusion of innovations theory (innovative networks) explores health care networks and their role in influencing the spread of new medical ideas and practices. Change agents and opinion leaders often play major roles in spurring the adoption of innovations, although factors inherent to the innovations also play a role. SNA has enjoyed extensive application in public health, particularly adolescent risk behaviours oriented toward substance use. According with Valente (2010), applications of SNA to public health and medical issues can be divided today into following areas:

Social support and its influence on mortality and morbidity - represent the largest area of application (Knowlton, 2003);

AIDS/STDs and family planning research - have benefited from network theory and modelling (Aral et al., 1999);

Community health projects - have used network analysis to improve message dissemination and program implementation (Stoebenau and Valente, 2003);

Inter-organizational collaboration, cooperation, and exchange studies - have been conducted to improve understanding of health service provision (Harris et al., 2008);

Understanding and improving health care provider performance (Soumerai et al., 1998).

Until now, none of the studies were dedicated to the particular field of health rehabilitation, leaving an important research gap that could be filled by researches like the one we conduct at the INRMFB.

2.2 Mapping of the knowledge flows in the target community of practice

This step of our study aims to produce an accurate picture of the knowledge flows that the target community identified in the medical area of rehabilitation process actually enacts in the accomplishment of its organizational objectives. The output of the mapping step will be: (a) the identifications of all the typologies and number of agents involved in the current process; (b) the mapping of the relationships among the subjects with a focus on the exchanges of information and knowledge taking place in the network. The above actions are performed by SNA through the following steps (Cross et al., 2002):

Identification of the target group: a number of four rehabilitation teams (clinics) with their patients were identified at the INRMFB – a unique rehabilitation hospital in Romania. In general it is recommended to pick up groups that cross physical, functional and even organizational boundaries; from this perspective, the INRMFB meet all these requirements.

Design the survey: in general different surveys may be designed to identify different kind of networks, such as (i) Information network, (ii) Advice network, (iii) Learning networks, etc. The

http://stat.gamma.rug.nl/stocnet/

http://www.analytictech.com/ucinet.htm



survey that we have designed particularly for the INRMFB is used to reconstruct the existing relational systems (see section 3 for of the paper).

Collect the data: the survey was distributed through the members of the CoP from the Rehabilitation Clinic no. 3 and 4 within the INRMFB. It is important to secure a very high response rate in the unit (between 80% and 100%) to not miss key individuals in the mapping.

The following set of attributes is considered in the survey to characterize a node to node link (N2N):

The object of exchange: which is the nature of exchanged information;

The frequency of exchange: how often interaction happens;

The medium of exchange: how exchange takes place? F2F conversations, telephone, email, formal communication (e.g. standard letter, forms, bulletins …). A medium continuum has been defined by Daft and Langel (1986) in which medium changes according to equivocality of information. The less equivocal is the nature of information that has to be exchanged, the more standardize and impersonal the communication medium is.

The development of the survey has taken into consideration variables operationalization and survey structures already developed in the SNA literature.

2.3 Analysis: Identification of critical aspects and areas of improvements

The surveys content will be used to produce sociograms that will be analyzed by using SNA software (NodeXL and UCINET). The software will produce graphical representations of the network structures and compute relevant structural indicators. These outputs will allow us to identify network roles and possible problems. Roles are identified in the literature in various ways. For example: a) Cross et al. (2002) base their definitions on the structural properties and they have found:

Connectors;

Boundary spanners;

Information brokers;

Peripheral specialists.

b) Gould et al. (1989), in terms of brokerage capability and function, identify:

Coordinator;

Gatekeeper;

Representative;

Itinerant brokers.

c) Rogers (2003) in terms of the function played by an individual in knowledge diffusion processes:

Innovators;

Opinion leaders.

d) While Gladwell (2000) reports about:

Connectors;

Salesmen;

Mavens.

Usually, typical organizations’ structural problems regard:

Presence of bottlenecks in the information/knowledge flows (e.g. individuals that are too central and suffer from work or information overload);

Lack of boundary spanners, i.e. individuals providing effective connection among diverse groups;

Presence of marginalized individuals whose more intense involvement in the process could be instead desirable;



Lower than needed connection density;

Lack of knowledge hubs;

Network fragmentation (presence of disconnected clusters).

By analyzing the actual structure of the four clinics from the INRMFB, one or more of these problems we expect to face. Given the particularities of this hospital, other structural issues also could be found.

2.4 Definition of the functional specifications for redesigning the agents, network and the functionalities of Knowbots

Once the critical aspects have been identified we will start the redesign of the network with the aim of developing an augmented socio-technical system both by increasing / reorienting / intensifying / introducing new knowledge flows, and through the introduction of intelligent agents as nodes whose role will be to provide knowledge and/or to solve structural problems in the existing organizational networks. For instance, we may find out that we need better communication among specialists in the different phases of the process for the purposes of a better traceability of the therapeutic process. Or we may discover that doctors need helps in some step of the diagnosis. Or that better patient-doctors communication has to be provided to support home care. Given the set of needs, we will define a set of priorities and focus. Then we will identify functional requirements for the design of intelligent agents (knowbots) starting from the general description and architecture developed in our previous researches. In fact, the Knowbot design will be an instantiation of the general knowbot model introduced in M<r<cine and Scarlat (2010). The augmented network will be represented in an agent-based model to simulate its behaviour and perform what-if analysis to check if the proposed technological solutions and the redesign of the overall network will achieve the expected performances, as specified by the beneficiary – the management team of the INRMFB. The output of the simulation will be used to support the design of the real knowbots.

3. Designing the survey for data collection

In order to give an answer to the first objective defined in section 2 we have created a survey that is used to reconstruct the existing relational systems from the INRMFB, including questions that collects some demographics, additional details to characterize the existing links within the network, and quantitative data. In addition to the traditional SNA data collection, we collect more qualitative information through interviews to each subject to elicit their perceptions about the quantity and the quality of the knowledge flows by implementing a knowledge audit (Liebowitz, 2010). The main sections of the survey are:

Section 1: Demographics: name, department, profession, no of years within INRMFB;

Section 2: Knowledge resources: knowledge resources types (databases, magazines, books, people – within INRMFB and external experts, Internet), and frequency of use;

Section 3: Knowledge use: directly retrieving answers to specific questions, analyze and/or synthesize to answer a specific question, routine and/or variable procedures, designing reports, educational or promotional materials. The questions in this section also include the ways people store and share within their peers the received / retrieved information;

Section 4: Knowledge flows: connections between the Knowledge categories and the Staff categories within or outside the INRMFB.

Section 5: Knowledge needs: main constrains in accessing different pieces of knowledge, risks faced by the critical knowledge within the INRMFB departments due to the turnovers in organization or lack of backup expertise in knowledge storage.

Within these sections, a set of questions were designed to capture information about the following issues:

Do you have enough information to do you job?

Which kind of additional knowledge and information would be helpful for you to have?

Through which channels and knowledge sources you fill these knowledge gaps?

Which are the criteria guiding knowledge search and evaluation?



As a node of the network we have also included the Internet, information systems and other IT-based devices or channels that members of the community may use to access information inside or outside their organization. For instance, it is reasonable to expect that members already access external knowledge sources through the Internet. That’s way we also expect to find that some of the tasks they perform manually could be delivered in an automated way or facilitated by the knowbots.

4. Data analysis for the Rehabilitation Clinic no. 3 and 4

Until now, the questionnaires were distributed through participants in all four Rehabilitation Clinics. At clinic no. 3 data collecting is now complete; we succeed to secure an 83.34% response rate in this unit (30 professionals out of 36), and we have already processed the data. In the Clinic no. 4 we have so far a 65% response rate and the data collecting is just at the beginning for clinics no 1 and 2. Since the results obtained analyzing data for clinic no 3 are very detailed and comprehensive (almost 25 pages were delivered to and analyzed with the clinic head), we have selected for this paper only some particular ones, relevant for our paper’s goals.

4.1 Knowledge networks for the Rehabilitation Clinic no. 3

Data collected through the field analysis have been used to model using SNA different kinds of knowledge networks within the team operating in the Rehab unit of the INFRMB (general advice, management knowledge, subject-matter expertise, knowledge about organizational procedures). The networks have been visualized through social graphs and we have performed an analysis of centrality. The objective of this analysis is to answer to the questions: ―Are there central actors in knowledge sharing? Who are the central actors?‖ For this purpose, we have used the measure of ―degree centrality‖, based on which we can assess if there is an actor more involved in knowledge sharing process and, consequently, represents a knowledge hub in the group. The degree of centrality can also be reflected in the distribution of links in the analyzed network; in the case in which a few nodes are very central this distribution tends to a power law.

4.1.1 Job in general network

First, we have considered the network composed by all the people mentioned by interviewees in questions from the second section of the survey. We name this network ―Job in general‖ network (figure 3). This network is actually the aggregation of the following knowledge networks: ―General Advice‖ Network; ―Management and leadership knowledge/advice‖ Network; ―Subject-matter expertise/content knowledge‖ Network; and ―Knowledge about the organization and its procedure‖ Network. The red circles are interviewed actors worked in Clinic 3, blue squares are actors working in INRMFB but in other Clinics, black up-triangles are external experts. From this network we can infer to which extent the team members search for knowledge outside their team and organization and if there are external experts who are central and more involved in knowledge sharing process. Considering the involvement of internal and external experts we have:

The search for specific expertise involves a high number of subjects and most of them are external to the organization;

While knowledge is shared with external experts, experts are not shared! Actually, more than 90% of them have just 1 inbound link. This means that the each team member accessing external expertise has his/her personal network of contacts and those are not shared with team members;

The frequency of exchange is reasonably high: on average 52 % of experts are involved monthly, 18% weekly, 24% quarterly, 3% between monthly and weekly, 3% between quarterly and monthly.

The job in general overall network exhibits three important characteristics:

Internally, knowledge is shared in a centralized network characterized by the presence of a few hubs and in which the knowledge distribution seems quite inhomogeneous, with a substantial presence of marginal actors;



Externally, the team members consult with a high number of experts but this sources of knowledge belong to personal networks and are not shared;

There is a high degree of correlation between in-degree centrality and number of external links: this implies that those who are more engaged into internal knowledge sharing are also those who consult more with external experts. These subjects play at the same time the role of internal knowledge hub and the role of external knowledge brokers that are able to acquire new knowledge from the external environment and diffuse it in the team.

A

D

B

C

The ―Job in general‖ network for Clinic no 3

Figure 3: The ―job in general‖ network for Clinic no 3

4.1.2 General advice network

Considering again all the actors we obtained the following network that display the ways team members of the clinic no 3 seek for general advices in performing their activity (the size of each node is proportional to its degree centrality). We have the following results:

Also in this case the network is very centralized (power law distribution);

The actors who are more involved in knowledge sharing process are the following, namely for privacy actor A, actor B, actor C and actor D:

There is an intensive use of external experts to get general advice.



D

C

A

B

Figure 4: The ―general advice‖ network for Clinic no 3

Actors # Inbound links

Actor A 16

Actor D 16

Actor C 10

Actor B 9

4.2 Knowledge sources and use

Because the SNA does not tell much about the ways participants use that knowledge, the motivations behind knowledge exchange, and the non–human knowledge sources used by subjects to perform their work, additional analyses have been carried out on the network humans/knowledge tools and resources, the organizational roles that are more involved in knowledge sharing, and the knowledge tasks that are more or less routinely performed by workers. Specifically we tried to answer to the following questions:

Do the team members use the same knowledge tools to perform their work?

Do they use the same information or knowledge resources to perform their work?

Which knowledge tasks they use their knowledge for?

Which organizational roles are more involved in knowledge exchanges?

Concerning the second question, for example, we have found that knowledge resources include a variety of sources: medical journals, books, internet, videos, etc. The difference with knowledge tools is that resources are just passive repository of potentially useful data but they are not used to perform work tasks, if not indirectly.



The empirical data (figure 5) shows that, unlike with knowledge tools, knowledge resources are used very frequently, though mainly only by doctors. The fact that other categories of workers do not use knowledge resources can be explained in several ways: i) their work is less knowledge intensive; ii) there is a lack of knowledge resources specifically developed for less skilled workers; iii) less skilled workers obtain the knowledge they need directly form more skilled workers acting as knowledge brokers.

Legend: 5 for daily, 4 for weekly, 3 for monthly, 2 for quarterly, 1 for other, 0 for never

How many actors

use it? % of total

actors Average

frequency

KR: MEDLINE PLUS JOURNAL 5 0,22 3,80

KR: MEDICAL AGENDA 11 0,48 3,73

KR: REHAB MED-DE LISA JOURNAL 2 0,09 3,50

KR: EUROPEAN JOURNAL OF REBAH 5 0,22 3,60

KR: REHAB MED FRONTERA JOURNAL 2 0,09 1,50

KR: "ASUL VERDE" Journal 13 0,57 3,08

KR: "URGENTE MEDICO-CHIRURGICALE" 9 0,39 3,56

KR: REHABILITATION COMPENDIUM 6 0,26 3,67

KR: Medical Books 12 0,52 3,416

KR: Internet 14 0,61 4,285

KR: Medical TV documentaries 3 0,13 3

KR: other 10 0,43 1,5

Figure 5: Knowledge sources and their use

In order to check if different categories of workers have the same behaviour in the use of knowledge sources and tools, we have disaggregated our data and our sample articulated in the following categories:

Doctors;

Medical assistants;

Physiotherapists;

Physical-physioterapist;

Physical therapist;

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

0.00 0.20 0.40 0.60 0.80 1.00 % of total actors

Avera

ge f

req

uen

cy

KR: MEDICAL AGENDA

KR: REHAB MED-DE LISA JOURNAL

KR: EUROPEAN JOURNAL OF REBAH

KR: REHAB MED FRONTERA JOURNAL KR: "ASUL VERDE" Journal

KR: "URGENTE MEDICO- CHIRURGICALE" KR: REHABILITATION COMPENDIUM

KR: Medical Books

KR: Internet

KR: Medical TV documentaries

KR: other

KR: MEDLINE PLUS JOURNAL



Nurses;

Statistician;

Clerk;

Other.

In order to compare the importance of a knowledge source we consider that a knowledge source is important if it is shared by at least 50% of categories and it is used at least monthly. In the figure 6, we consider the average frequency, and we obtained the following data (a red box means that nobody in that category use that tool, while the green box means that more than 50% of members of that category use that tool with a frequency greater than 3):

Do

cto

r

Med

ica

l a

ssis

ta

nt

Ph

ysio

th

era

pis

t

Ph

ysic

al-

ph

ysio

th

era

pis

t

Ph

ysic

al-

th

era

pis

t

Nu

rse

Sta

tis

tic

ian

Cle

rk

Oth

er

INRMFB wide-database 4,00 5,00

INRMFB web page or Intranet 3,67 4,40 4,00 4,00

The Clinic’s database My own database or contact list file 5,00 4,00 4,00 5,00 5,00

The database of another workplace 4,00 4,00 4,00

INRMFB policy/procedures manual or guidelines

Clinic’s specific procedures manual or guidelinesOther procedures manual or guidelines 4,00 3,60 ### 4,00 0,00 4,00

Own notes or procedures 4,67 4,00 4,00 5,00 5,00

Figure 6: The importance of knowledge sources for categories of medical staff

The institutional tools (the clinic database, INRMFB wide-database, clinic’s specific procedures manual or guidelines, INRMFB policy) are used by almost all categories but with a low frequency. All categories (excepting the Nurses) prefer to use their own databases or procedures. The Nurses and the Physiotherapist do not use any tool at all, while doctors are those who make more frequent use of diverse tools.

4.3 Knowledge flows

The main findings obtained by the section 4 in the survey concern: types and direction of knowledge flows and suggested improvements, the aim being to identify:

Which are the knowledge categories that are more shared and with whom;

How the knowledge flow can be improved according to the suggestions provided by the interviews;

Which are the obstacles that make knowledge sharing less efficient, and

Which kind of critical knowledge is at risk to be lost or forgotten.

This part of the survey uses open questions. In order to perform the analysis, we have analyzed the text contained in each answer to identify recurrent factors and group them into higher kevel categories. The knowledge categories that are shared among the participants are:

Scientific knowledge (Medical knowledge, new rehab techniques, news & updates, case studies, educational);

Practical knowledge (Medical practice, Routine tasks, massage techniques, patients care, training);

Managerial knowledge;

Administrative knowledge;

Social (conversations, relationships, team building).



The subjects with whom the above types of knowledge are shared are:

Physicians;

Trainees (Young doctors, Students, Residents MDs);

Medical assistant;

Physiotherapists (includes the various types, e.g. masseurs);

Managerial staff;

Administrative staff (e.g. statistician, clerks, …); Nurses;

Patients’ family, friends and volunteers.

The data show that practical knowledge is deemed as more critical by the interviewees compared to other categories as social, managerial or administrative. Also interesting is that social knowledge, here intended above all in terms of the ability to deal with patients and their families as well as to build trust and reciprocity with the colleagues internally, is perceived critical only by the medical assistant and the physiotherapists consider important scientific knowledge as well as practical. Furthermore, managerial knowledge category is perceived as critical only by doctors.

Knowledge Category Ph

ysi

cia

ns

Med

ica

l

Assis

tan

t

Nu

rse

Ph

ysi

oth

era

pis

t

Ma

na

geri

al

an

d

ad

min

istr

ati

ve

sta

ff

Yo

un

g d

octo

rs,

Stu

den

ts,

Resid

en

ts M

Ds

Pat

ien

ts’ f

am

ily

an

d f

rien

ds a

nd

vo

lun

teers

Practical (19) 0,37 0,58 0,58 0,42 0,05 0,21 0,58

Managerial (2) 0,50 1,00 0,50 0,50 1,00 0,00 0,00

Administrative (4) 0,00 0,00 0,00 0,00 1,00 0,25 0,00

Social (1) 1,00 1,00 1,00 1,00 1,00 1,00 1,00

Scientific knowledge

(9) 0,44 0,67 0,67 0,44 0,00 0,78 0,00

Staff-Knowledge Category

The following table reports data about the relationship between knowledge categories and staff types that are the target of knowledge sharing. In practice the table helps to answer to the question: ―Which types of knowledge are critical and with whom are they shared?‖ For example, out of 19 interviewees that have answered to this part of the survey reporting as critical the ―practical‖ knowledge, 37% of them share this type of knowledge with physicians. We can observe that only for the administrative and managerial knowledge category all actors have a similar thinking while for the other knowledge category we observe different thinking. The data show that practical and scientific knowledge are the most mentioned (by respectively 19 and 9 subjects). With the exception of the management and administrative staff, practical knowledge is widely shared among all staff categories. Scientific knowledge instead is transferred only between medical personnel.

4.4 Findings based on the sections 4 and 5 of the questionnaire

The analyze of the actual structure of the INRMFB and especially of the Rehabilitation Clinic no. 3 and 4, based on the section 4 and 5 of the designed survey, has conducted us, so far, to the following findings / conclusions:

The Rehabilitation Clinics no. 1 and 2 are placed into a different location than the Clinics no. 3 and 4. This makes the communications among the four medical CoPs an exception instead of a frequent habit;

Among the four clinics and also among them and the management of the INRMFB the communication is based on the old classical methods - printed documents transfer;

The INRMFB has no Intranet. Due to the distances among the fourth clinics, the absence of the Intranet makes the information/knowledge transfer very slow, and often produces losses of



valuable information/knowledge for some of (or all) the clinics, and also for the managerial team of the hospital;

The spatial separation of the INRMFB’s departments has also effects over the results from the research activity of the Institute. Within this particular hospital there are several research team: (1) Balneo-climatology; (2) Cellular breeding; (3) Mineral waters and therapeutic mud testing; (4) Immunology lab; (5) Bacteriology lab for mineral waters. In the actual organizational structure, these labs and scientific teams rarely communicate among them and share their activity results;

There are no virtual libraries or repositories within the INRMFB. So, the only categories of medical staff that can access these knowledge resources are those doctors who are also professors at the Bucharest ―Carol Davila‖ Medical University which has its own such library. The absence of virtual repositories results sometime in loosing important documents, patients’ medical files, tests results or managerial decisions transcripts.

There are no policy/procedures manual or guidelines neither within the INRMFB or within the clinics / departments;

There are no real boundary spanners, the effective connection among diverse groups within the Clinic no 3, for example, this task being (informally) on charge of the chief of the clinic. Because of this, the chief of the clinic is overloaded with her work and with administrative and information sharing tasks;

In order to benefit for a second opinion (when need it), doctors within Clinic no. 3 usually contact they own personal connections via Internet (often foreign specialists) rather than call a fellow colleague who works into another clinic within the INRMFB;

One interesting aspect for the Rehabilitation Clinic no 3: each month the chief of the clinic organizes a so-called ―essay day‖ when all the doctors, the residents and the therapists are involved in presenting a paperwork describing the most recent events / aspects / achievements of their activity in the hospital. This method is used in order to exchange knowledge, but also to improve the communication techniques of the Medical Staff.

The absence of the two categories of specialists – the social workers and the occupational therapists – hardeners the process of medical rehabilitation for both CoPs – Medical CoP and Patient CoP. Doctors are forced by this circumstance to involve them selves in the process of social reintegration of the impaired people, both by psychologically assisting the patients, and by dedicating a lot of time in educating and training the patients’ families for coping with their disabled family member condition.

With respect to the obstacles in the knowledge flow, about 61% of actors identify the most important obstacles for knowledge sharing in organizational factors, and primarily organizational culture and work organization. Also, almost all the actors indicate as knowledge at risk of lost the ―Documents and medical data concerning the patients and their rehabilitation progress‖.

5. Conclusions and future developments

The analysis presented above is based on a single case study and the findings cannot be generalized to represent typical ways in which Rehab teams work, exchange or create knowledge. However, the generalization of the results lie outside the scope of our work while the primary objective is to use the findings of the analysis to inform the redesign of the network and the introduction of knowledge tools and intelligent agents able to alleviate criticalities and make knowledge flow more efficient and fluid. In this respect, we list in the following the characteristics of the knowledge networks emerged from the field analysis. Its can be analyzed to identify opportunities to improve the efficiency and effectiveness of knowledge sharing through the redesign of the organizational network. Such a redesign involves the identification of a socio-technical network made up by human and non human agents (as showed in figure 7). First of all, we can assert that the classical organizational structure of the Rehabilitation Clinic no. 3 (as well as of the entire INRMFB) needs to be improved both physically and virtually:

The real structure has to be improved by hiring professionals for social workers and occupational therapists positions. This could be done immediately by launching a call for specialists on the European and/or international labour market. A better solution which nevertheless requires more



time consists in enrolling Romanian students within the EU universities that provide academic programs on these particular professions;

Virtually speaking, the organizational structure of the Clinic no. 3 has to be augmented with Intranet that needs to be populated with virtual species like Knowbots. These virtual agents can facilitate the knowledge creation, retrieval and sharing inside the Medical CoPs, among these and the managerial team of the INRMFB, and also among the human resources of the hospital and the (social, medical, and economical) macro environment. Knowbots should have a particular role in create for the Patient CoP useful knowledge that can help them in the rehabilitation processes.

the –

KNOWLEDGE

AGENTS (Knowbots)

PATIENT

Occupational

therapists

Medical staff

Medical Rehabilitation Clinic No. 3

Medical staff

Medical staff

Social

workers

Figure 7: The new knowledge-oriented (augmented) structure of the Rehabilitation Clinic no. 3 of the INRMFB-micro level

Secondly, the results of our study highlighted some interesting characteristics and allowed us to identify a number of opportunities for possible improvements. 1. In terms of Knowledge centralization, the data shows that all the knowledge networks in the case are heavily centralized and that the several sub-networks are always centered on the same hubs. The individuals playing the role of hubs are also charged with management functions, and they are also the ones who are most connected with external experts. They play the role of brokers diffusing internally knowledge that is acquired externally. The opportunities for improvements are:

Redesign organizational roles and processes so to favour the decoupling of brokerage from hubs;

Create local knowledge bases of procedures and practices: the high indegree level of the most central nodes is due to the need to search for help from more skilled workers.

2. Concerning the external experts, the analysis shows the individuals that are more connected internally are also those who are more connected externally, especially when it comes to subject-matter expertise. Besides, the sources of expertise are not shared and seem to belong more to personal than to inter-organizational networks. This lack of sharing identifies as opportunities for improvements:

At least for specific cases and sub-practices, it can be suggested to develop expert systems able to provide answers more quickly and economically than external experts;



Encourage the use of existing social media professional platform to support team members in sharing contacts and membership to groups and communities of practices;

Start a wiki for the collaborative description and solutions of problems. The wiki could contain a repository of the questions that are made to external experts as well as the answers that are collected and the name of the experts.

3. There is clearly a lack of sharing in terms of knowledge tools and resources. Conversely, almost all the team members do perform cognitive tasks of the same types (though arguably at different level of complexity). Thus, while cognitive work is widely performed, it is not supported by tools or it is limitedly so through the use of personal tools. The opportunities for improvements here are:

Make a knowledge elicitation exercise to assess if some of the tools invented and used at the personal level can actually be improved and implemented at the team level or organizational;

Implement the intranet and provide all team members with mobile internet access allowing workers to access knowledge and data on the go;

Create an information systems able to track and monitor the entire medical life of a patient from the beginning of the treatment until to the dismissing;

Encourage the use of web 2.0 tools to create bottom up shared knowledge repository, e.g. collections of video, articles, publications, presentation and other useful materials tagged with relevant keywords to facilitate search and reuse;

Use eLearning to reduce knowledge gap in the team and increase the absorptive capacity of less skilled members.

The representation in figure 7 is the general frame under which we will further develop our study following the three main aimed objectives enounced in section 2 of the paper. Within the new knowledge-based organizational structure, one interesting idea open for discussion is which are more important to focus on, nodes (human and virtual agents), or links between them (direct / indirect connections and/or relationships)?

Acknowledgements

This paperwork was supported by CNCSIS - UEFISCDI, project number PNII - IDEI 810/2008.

References

Aral, S. O., Hughes, J. P., Stoner, B., Whittington, W., Handsfield, H. H., Anderson, R. M., and Holmes, K. K. (1999), „Sexual mixing patterns in the spread of gonococcal and chlamydial infections‖, American Journal of Public Health, 89, 825-833.

Cross, R., Nohria, N., and Parker, A. (2002), "Six myths about informal networks - and how to overcome them", MIT Sloan Management Review, vol. 43, no. 3, pp. 67-75.

Daft, R. L., Lengel, and R. H. (1986), "Organizational information requirements, media richness and structural design", Management science, vol. 32, no. 5, pp. 554–571.

Dolfsma, W. (2008), Knowledge Economies. Organization, location and innovation, Routledge, Taylor and Francis Group, London and New York.

Gladwell, M. (2000), The tipping point, New York: Little Brown and Company. Gould, R. V, and Fernandez, R. M., (1989), "Structures of mediation: A formal approach to brokerage in

transaction networks". Sociological Methodology, vol. 19, no. 1989, pp. 89–126. Harris, J. K., Luke, D. A., Burke, R. C., and Mueller, N. B. (2008), „Seeing the forest and the trees: Using network

analysis to develop an organizational blueprint of state tobacco control systems‖, Social Science & Medicine, 67, 1669-1678.

Iandoli, L., Ponsiglione, C., Zollo, G. (2010), ―Modelling networked cognition: a socio-computational approach‖, Journal of Economic Computation and Economic Cybernetics Studies and Research nr. 4/2010, vol. 44, pp. 121-142.

Knowlton, A. R. (2003), ―Informal HIV caregiving in a vulnerable population: Toward a network resource framework‖, Social Science and Medicine, 56, 1307-1320.

Liebowitz, J. (2010). ―The Quick Basics of Knowledge Management‖, in Liebowitz, J., Schneider, R., Andreadis, J.: Knowledge Management in Public Health, Taylor & Francis.

Marın C., Stalker I., and Mehandjiev N. (2008) „Engineering Business Ecosystems Using Environment-Mediated Interactions‖, in Weyns, D., Brueckner, S.A. and Demazeau. Y. (Eds.): EEMMAS 2007, LNAI 5049, pp. 240–258, Berlin: Springer-Verlag, Available: http://www.springerlink.com/content/mn877m1970674qr4/.

M<r<cine V. and Scarlat E, (2009), „Dynamic Knowledge and Healthcare Knowledge Ecosystems‖, Electronic Journal of Knowledge Management, Published by Academic Conferences Limited, Volume 7, Issue 1, 2009, [online], http://www.ejkm.com/volume7/issue1, pp. 99-110.

http://www.springerlink.com/content/mn877m1970674qr4/

http://www.ejkm.com/volume7/issue1



M<r<cine, V. and Ianole, R. (2010), „How Knowledge Determines Demand Dynamics - A New Perspective for Evaluation. Study Case for Healthcare Services‖, Journal of Economic Computation and Economic Cybernetics Studies and Research nr. 4/2010, vol. 44, pp. 5-22.

M<r<cine, V. and Scarlat, E. (2010), „Knowledge Ecosystems’ Development in Business and Healthcare Using Knowbots‖, Proceedings of the 11th European Conference on Knowledge Management, Vila Nova de Famalicao, Portugal, 2-3 September 2010, published by Academic Publishing Limited, Reading, UK, pp. 663 – 675.

Pór, G. (2000) ―Designing knowledge ecosystems for communities of practice‖, [online], www.co-i-l.com/coil/knowledge-garden/dkescop/kmo.shtml.

Rogers, E. M. (2003), Diffusion of Innovation, 5th edition, New York: Free press. Soumerai, S. B., McLaughlin, T. J., Gurwitz, J. H., et al. (1998), “Effect of local medical opinion leaders on quality

of care for acute myocardial infarction: A randomized controlled trial‖, Journal of the American Medical Association, 279, 1358-1363.

Stoebenau, K. and Valente, T. W. (2003), “The role of network analysis in community based program evaluation: A case study from Highland Madagascar. International Family Planning Perspectives, 29, 167-173.

Valente, T. W. (2010), Social Networks and Health: Models, Methods, and Applications, New York: Oxford University Press.

http://www.co-i-l.com/coil/knowledge-garden/dkescop/kmo.shtml

http://www.co-i-l.com/coil/knowledge-garden/dkescop/kmo.shtml

ISSN 1479-4411 81 ©Academic Publishing International Ltd Reference this paper as: Paulin, D and Suneson, K. ―Knowledge Transfer, Knowledge Sharing and Knowledge Barriers – Three Blurry Terms in KM‖ The

Electronic Journal of Knowledge Management Volume 10 Issue 1

(pp81-91, available online at www.ejkm.com

Knowledge Transfer, Knowledge Sharing and Knowledge Barriers – Three Blurry Terms in KM

Dan Paulin and Kaj Suneson Department of Technology Management and Economics, Chalmers University of Technology, Gothenburg, Sweden [email protected] [email protected] Abstract: In the knowledge management world there are many different terms flying around. Some are more important and frequently used than others. In this paper, we present and discuss the development and views of three terms: knowledge transfer, knowledge sharing and knowledge barriers. Knowledge transfer and knowledge sharing are sometimes used synonymously or are considered to have overlapping content. Several authors have pointed out this confusion while other authors have attempted to clarify the differences and define the terms. Knowledge barriers as a term seem to have a partly more obvious content although the borders between knowledge barriers and connecting terms, such as „barriers to knowledge sharing’, seem to blur discussions and views. The aim in this paper is to make a contribution in finding appropriate demarcations between these concepts. After reviewing some Knowledge Management literature, it seems that the three terms, knowledge transfer, knowledge sharing and knowledge barriers, are somewhat unclear and has different meanings depending on the authors views. For knowledge transfer and knowledge sharing, the blurriness is linked mainly to the fact that the analytical level each term is related to has come and gone and come back again. For knowledge barriers, the blurriness comes from the development of the term. The mere existence of the many different categorizations of knowledge barriers implies that the concept itself is blurry. The concept seems clear cut and focuses on knowledge although it is also broad and later sources have included much more than knowledge. This paper concludes by highlighting the effects on the terms when two different knowledge perspectives, knowledge as an object (or the K-O view) and knowledge as a subjective contextual construction (or the K-SCC view) are applied. The clarifications are supported by examples from companies in different industries (such as Cargotec and IKEA) and emergency services. Keywords: knowledge barriers, knowledge management, knowledge sharing, knowledge transfer

1. Introduction

During the last ten years numerous publications dealing with knowledge management-related issues have been published in journals ranging from Conservation Biology, Post-Communist Economies, Childhood and European History Quarterly to more business-oriented journals such as Research Policy, Journal of Knowledge Management, Harvard Business Review and KM World. It can be argued that in aiming for efficient Knowledge Management (KM), the search for ―correct‖ choices of methods and steps is crucial. These choices require a well-defined taxonomy with clear concepts and terms. The content and meaning must be clear cut and there should be no ambiguity about the aim when fundamental concepts are used. Although this is undoubtedly a desirable objective, it is hardly the current state of affairs regarding commonly used terminology in KM. In many cases, the authors use central terms interchangeably and without making a distinction between them and sometimes without sufficient explanation of from which perspective the terms are used. A fundamental part in knowledge management is to spread and make knowledge accessible and usable within or between chosen organizations. When reviewing KM literature, there are some terms that seem more central and fundamental than others. For example, in the view of the knowledge-based firm creation, coordination, transfer, and integration of knowledge creates competitive advantages for firms (Ghosal and Moran 1996 (in Sambamurthy and Subramani (2005))). When King (in Schwartz (ed.) 2006) in addition to the statement above, proposes that knowledge transfer (KT) is a fundamental process of civilization and that it is central to learning which in turn is critical to development, there is clear support for exploring the term knowledge transfer. KT is sometimes used interchangeably with knowledge sharing (Jonsson 2008), so in order to explore knowledge transfer, knowledge sharing (KS) should not be ignored. Riege (2005; 2007) argues that the barriers affecting KS and KT have received little attention at the same time that they have a negative effect on KM and its possibilities to deliver a positive return on investment.





Another type of barrier in connection to knowledge was presented by Attewell (1992). He discussed the importance of knowledge barriers (KB) to understand interruptions or slow dissemination of innovations and how KB can be passed or lowered. Attewell (1992) presented KB as lack of knowledge about the technology and how this technology can be applied in an organizational setting. This can be interpreted as if KBs consist of two dimensions.. First, that it is hard to use a system if the knowledge of how to control and use it is lacking. This is a type of knowledge that is tightly connected to the system and its features. Second, it is a type of knowledge of how to implement the use of the technology in the processes of the specific organizations. This is a different type of knowledge where the connection between the organization and the system is not always obvious. In this regard, KB:s are acting as a perceptual stop. Where there is a KB, new information cannot be understood or interpreted. Even if the functions of the system are known it does not matter as long as knowledge about how to implement it in the organization is not there and vice versa. Here it is assumed that there is a tight connection between the knowledge in an innovation and the KB. By analogy the term KB is also important to discuss when considering dissemination of knowledge in general as well. The aim in this article is to discuss terms central to the dissemination of knowledge, to contribute to consciousness about the importance of clarity when using the concepts and to find appropriate demarcations between the concepts. KT, KS and KB are central in considering dissemination of knowledge and therefore are the paper focused on these terms. As will be shown in this paper the terms KT and KS are sometimes used synonymously or have overlapping content. KBs in themselves seem to have a more obvious content in being some sort of ―lack of knowledge‖. When further examined how to overcome this lack of knowledge, the solutions are quite different depending on what is meant by the term knowledge barrier. In some cases the border between KBs and connecting terms, such as ―barriers to knowledge sharing‖ is very close. In other cases it has another meaning which makes discussions and views blurry if this is not thought through and stated carefully. The paper is structured in the following way. First, examples from literature are shown to illustrate how this blurriness might be seen. Secondly, the development/change in the use of the terms is shown by presenting findings in literature, related to research in the KM area. Thirdly, key similarities and differences between uses of the terms are presented and discussed. Here, different views of knowledge are introduced using real-life examples since it is fundamental to the interpretation of these terms. Finally, the effects of the different views are discussed.

2. Problem definition

The starting point for the argument is to present examples of articles that state this blurriness, authors that use the terms without any clear distinction, books that use different (and overlapping) definitions in different parts of the text and authors who have different interpretations of these three terms.

2.1 KT and KS

In an article published in 2008, Anna Jonson points out this blurriness by stating: ―Within the frame of reference both „knowledge sharing’ and „knowledge transfer’ are used and discussed interchangeably. As it is not clear if there is a difference, both terms will be used.‖ (Jonsson, 2008: 39). Another example is ―… many authors and researchers have failed to provide a clear-cut definition for knowledge transfer and, at times, it has been discussed together with the term ―knowledge sharing‖‖ (Liyanage, et al., 2009: 122). There are authors that use both terms when discussing the same concept. For example, one author identifies over three dozen knowledge-sharing barriers in one article (Riege, 2005). In a more recent article, the same author uses the term knowledge transfer when suggesting actions to overcome the same and similar barriers (Riege, 2007). He even refers to his own research in the following way: ―Indeed, organizations wishing to make their knowledge management strategy a success need to pay attention to a potentially more than three dozen human, organizational and technological obstacles to transferring knowledge (Riege, 2005)‖ (Riege, 2007: 50).

Dan Paulin and Kaj Suneson


A third clear example of the blurriness is taken from The Encyclopedia of Knowledge Management (Schwartz, 2006) in which several definitions of knowledge sharing, knowledge transfer and knowledge sharing barriers are presented. All of the following quotations are taken from this encyclopedia. Knowledge sharing is defined, for example, as:

―The exchange of knowledge between and among individuals, and within and among teams, organizational units, and organizations. This exchange may be focused or unfocused, but it usually does not have a clear a priori objective.‖

―An exchange of knowledge between two individuals: one who communicates knowledge and one who assimilates it. In knowledge sharing, the focus is on human capital and the interaction of individuals. Strictly speaking, knowledge can never be shared. Because it exists in a context; the receiver interprets it in the light of his or her own background.‖

The differences between the definitions of knowledge transfer are perhaps even clearer.

―Includes a variety of interactions between individuals and groups; within, between, and across groups; and from groups to the organization.‖

―The focused, unidirectional communication of knowledge between individuals, groups, or organizations such that the recipient of knowledge (a) has a cognitive understanding, (b) has the ability to apply the knowledge, or (c) applies the knowledge.‖

Contradictions and discrepancies between the definitions can be found on several levels:

Sharing taking place between individuals only versus between individuals, teams, units or organizations

Focused or unfocused versus clearly focused

A transaction versus saying that knowledge can never be shared

Unidirectional versus multidirectional

2.2 KBs

One author that made the concept of knowledge barriers known was Attewell (1992) when he referred to knowledge barriers as „lack of knowledge’ about a new technology and how it should be used in organizations. The concept was then used to explain why a specific technology (in that case business computers) did not spread. The ―lack of knowledge‖ element in KBs seems to be rather consistent in literature but what that really means seems to differ somewhat. In literature, knowledge barriers seem to have been applied from at least three different views: 1. Lack of knowledge about something depending on barriers for knowledge sharing or transfer. 2. Not enough knowledge depending on level of education in a certain area or about a particular topic. 3. The perceptual system in a specific human or group of humans does not contain enough contact points, or does not fit incoming information to utilize it and convert the information to knowledge. These views are not always easy to distinguish between and sometimes they can be seen more as a scale than being fixed categories with clear boundaries. Depending on which view that is applied, important factors of how to ―solve‖ knowledge barriers are implied. An example of the first view is when Bundred (2006) exemplifies that knowledge barriers is created when senior staff is reluctant to share knowledge with junior staff in the public sector. In the article the knowledge barrier is only discussed as information not shared between silos. The suggested solutions are primarily aimed at overcoming information sharing boundaries (or knowledge sharing boundaries as transporting the knowledge from one place to another) of different kinds. Szulanski (2003) uses the concept ―knowledge barriers‖ to describe a set of factors that explains why knowledge might not transfer. This makes it easy to believe that there is a tight and immediate connection between a company’s efforts to reach knowledge transfer and the concept of ―knowledge



barriers‖. Although he focuses on transfer these barriers are exemplified with the recipients’ level of knowledge prior to the transfer, how well the transferred practice is understood in the organization, and the ability to unlearn. In an earlier paper, Szulanski (1996) refers to three constructs as knowledge barriers, namely the absorptive capacity of the recipient, casual ambiguity and an arduous relationship between source and the recipient. One interpretation of the descriptions of KB:s is, in Szulanski's view, something that disappears if the correct knowledge is given to the recipient and when all pieces are presented to him/her the puzzle can be solved. An example of the last base might initially be Attewell (1992) in the part when the technology fit, the organizational structure and its processes has to be fitted together. Saemundsson and Holmén (2007) discuss creative processes starting when KB:s are lowered or disappear. This is possible because other knowledge that the entrepreneur has access to can be utilized. Indirectly is this a sign of a connection between the entrepreneurs’ perception and thoughts of the world and the disappeared KB. These discrepancies, differing views and contradictions create blurriness which will have an effect on the conclusions and recommendations provided by authors using these definitions in the same way that a perfectly engineered building might crumble to dust if its foundation is not solid.

3. Development of the terms knowledge transfer and knowledge sharing

In the first part of this section, we try to show the emergence, reemergence and development of KT and KS. Figure 1 is an attempt to visualize the different authors’ use of the terms with regards to their level on an individual-industry scale and the publication year. This is followed by the development of KBs.

3.1 The emergence and of KT and KS

Knowledge, its definition, source and method in which it is acquired has been discussed (at least) since the time of the philosophical debates by Aristotle and Plato. We would, therefore, propose that the initial emergence of the terms comes from these discussions and that the suggestions on how to deal with efficient and effective knowledge transfer and sharing has been ongoing to a varying degree of intensity since then. The reemergence of the terms can be traced to two different streams of research. The first can be found in product innovation and technology transfer literature in which the relationship and communication between units have been studied (e.g. Allen, 1977; Clark and Fujimoto, 1991). The second stream is based on the writings of Michael Polanyi and the terms tacit and explicit knowledge. In an influential Harvard Business Review article, Ikujiro Nonaka touches on the issues of KT and KS, even though he does not mention them explicitly. He writes ―Explicit knowledge is formal and systematic. For this reason, it can be easily communicated and shared…‖ (Nonaka, 1991: 98). Later in the same article, he says ―This helps create a ―common cognitive ground‖ among employees and thus facilitates the transfer of tacit knowledge.‖ (Nonaka, 1991: 102). These two streams have, to some extent, merged after Nonaka’s original article. Since that article and later articles and books by him (such as Nonaka and Takeuchi, 1995), in which they say that KS is a critical stage in KT) have had a strong impact on the research community, we regard this as the starting point for the reemergence of KT and KS as we know them today. Since then, the terms have developed gradually and extensively. Initially, the terms were used interchangeably (e.g. Badaracco, 1991; Hansen, 1999) but lately there has been an ongoing separation between them, which we will demonstrate in the following sections.

3.2 The development of KT

During the first years after its reemergence, KT was usually treated in line with the notion of the knowledge-based theory of the firm (Kogut and Zander, 1992; Grant, 1996). One of the most commonly cited authors here is Szulanski, who in numerous books and articles has developed the notion of KT, especially regarding intra-firm knowledge. His early work clearly states that knowledge is regarded as a firm’s stock (Szulanski, 1996).



During the late „90s and early „00s, the focus within this area remains on the strategic level with authors who address the question of the role of weak ties in sharing (!) knowledge across organization subunits (Hansen, 1999), others who focus on intracorporate knowledge flows within multinational corporations (Gupta and Govindarajan, 2000) and others who study business unit innovation and performance (Tsai, 2001). One noticeable exception is when the psychological and sociological aspects of this issue merge into the research stream when the effects from extrinsic and intrinsic motivation in individuals on KT within a firm are studied (Osterloh and Frey, 2000). During this time period, there is a switch from conceptual and theoretically-oriented research towards more empirically centered research. Paulin (2002 and 2006) studies KT processes in the automotive industry with a particular focus on the production process verification process. Schlegelmilch and Chini (2003) present a literature review in which the literature referred to (mainly from 1997 to 2002) predominates primarily in the direction of empirical studies. More recently published reviews on knowledge transfer still align to the higher level of analysis. Both the review by Easterby-Smith, et al. (2008) and van Wijk, et al. (2008) have a clear focus on intra- and/or inter-organizational knowledge transfer. However, Easterby-Smith, et al. (2008) identified a number of questions of both theoretical and practical significance to the current research frontier within the area of inter-organizational knowledge transfer and in their question ―How does the process of knowledge transfer unfold at different levels of analysis?‖ they also open up for analysis on the individual level. This diversion from the main track is continued by Liyanage, et al. (2009), when they state that ―knowledge transfer is the conveyance of knowledge from one place, person or ownership to another.‖ (Liyanage, et al., 2009: 122).

3.3 The development of KS

In the early work presented after Nonaka’s HBR article, KT and KS is used interchangeably with predominance towards KT. One author that adopts the term KS is Appleyard (1996). Here, she includes both comparisons on the industry level of interaction (by comparing KS in the semiconductor industry with KS in the steel industry) and on a national level (Japan is compared to the US) using individual respondents. Other researchers in the same stream are Dyer and Nobeoka (2000). Their findings include the statement that Toyota’s relative productivity advantages are explained in part by their ability to create and sustain network-level KS processes. Other perspectives that are strong in the KS stream of research are the psychological and the sociological. Cabrera and Cabrera (2002), for example, include the psychological notion of social dilemmas when analyzing the inclination of individuals to share knowledge with other individuals regardless of the fact that the company that they work for has invested in specific technology to enable such knowledge sharing. Fernie, et al. (2003) has a strong focus on personal knowledge. They argue that knowledge is highly individualistic and that it is embedded in specific social contexts. This article is a good example of the direction within knowledge sharing that is focused on the individual level – context-specific subjective knowledge. Another example of this stream is when KS between individuals in organizations is examined (Ipe, 2003). Here, four major factors that influence KS are identified: 1) The nature of knowledge, 2) The motivation to share, 3) The opportunities to share and 4) The culture and the work environment. In a recently published article, an in-depth review of articles on individual-level knowledge sharing is presented (Wang and Noe, 2010). They state that their article is the first to systematically review individual knowledge sharing and that previous reviews have focused on technological issues of knowledge sharing or knowledge transfer across units or organizations, or within inter-organizational networks. Areas of previous studies are:

Organizational context (including organizational culture and climate, management support, rewards and incentives and organizational structure)

Interpersonal and team characteristics (including team characteristics and process, diversity, social networks)



Cultural characteristics

Individual characteristics

Motivational factors (including beliefs of knowledge ownership, perceived benefits and costs, interpersonal trust and justice and individual attitudes)

To summarize sections 3.2 and 3.3, a visualization of the different authors’ use of the terms based on the organizational level is shown in Figure 1 below.

Figure 1: Different authors’ use of the terms with regards to their level on an individual-industry scale and the publication year

3.4 The emergence and development of KBs

During the '90s, the spread of computers and the computerization of industry were seen as important. In this setting, Paul Attewell studied factors that inhibited the spread of computer technology in companies. He found that when the companies lacked knowledge of how to use the technology, the possibilities inherent in the technology and the efforts to maintain the technology in the company became barriers to the use of the technology (Attewell, 1992). Although Attewell’s work is important in stating the content and highlighting the term, it has been known and used before in many different settings. For example, a quick search on Google scholar shows that it had been used to discuss the construction of a theory for socialist economy (Zielinski, 1962), Caldwell (1967) used it in a discussion on how knowledge set up a barrier to its own development and in another setting Ramaswami and Yang (1990) claimed that knowledge barriers affected the potential of companies to export. A similarity here is that knowledge barriers are regarded as a lack of knowledge, which leaves a person beyond all hope of grasping the content of the subject that is being discussed. The lack of a frame of reference from memories and experiences makes the topic impossible to understand or to connect to previous knowledge. In 1996, Szulanski presented the concept of stickiness. The main purpose of his article was to explain why knowledge and skills might be difficult to transfer between persons, entities and organizations. Factors affecting such transfer were divided into motivational factors and knowledge barriers. Within knowledge barriers, three factors were identified: 1) Lack of absorptive capacity (in which lack of knowledge is a part). 2) Causal ambiguity – uncertainty regarding how aspects of the knowledge interact and respond to factors in the environment as well as uncertainty if necessary factors are present in a given situation. 3) An arduous relationship between the source and the recipient. How easy or frictionless is the communication and intimacy between sender and receiver?



Later, knowledge barriers were divided into three different categories to explain problems in the adoption of complex technologies (Venkatesh, et al., 1999): 1) ―Backbone and connectivity‖ – Knowledge barriers to understanding the technology and regulations of how different technologies are permitted to be used, either separate or combined. 2) The need to understand customers’ equipment and the need for interoperability. Lack of such knowledge can be seen as a knowledge barrier on the supply side. 3) The understanding of how customers’ applications and services interact with technology and regulations. Tanriverdi and Iacono (1999) suggested that the technical knowledge barriers presented by Attewell (1992) should be expanded with three additional barriers in order to understand what inhibits the spread of telemedicine. These barriers are: 1) Economic – viewing the economic model in terms of the benefits gained by the organization. 2) Organizational – understanding how use of the technology fits into organizational processes. 3) Behavioral – the potential for the members in the organization to see how the technology functions in, and impacts on, their daily work. Building on Tanriverdi's and Iacono's work, Suneson and Heldal (2010) suggested that in situations when complex (information and communication) technology will be used jointly by two or more organizations, an understanding of the other organizations and their view of the technology might be needed for efficient use. Lack of understanding might act as an interorganizational KB that impedes co-operation.

4. Discussion

In the research streams presented, similarities and differences in use of the terms can be found. One common dividing line between KT and KS is related to the levels of analysis, in that KS is used more frequently by authors focusing on the individual level, while KT is used more frequently when groups, departments, organizations or even businesses are in focus (Argote and Ingram, 2000). This view can still be regarded as valid since there is support for this in a more recent review (Choo and Alvarenga Neto, 2010). However, one suggestion is that the main difference is derived from the basic view of knowledge. In a recent article, Sveiby (2007) focuses on two dominating views of knowledge and their influence on research. The two views are:

Knowledge as an object (K-O).

Sveiby (2007) exemplifies the stream of research based on this view with numerous references and points out relevant variations on this theme; knowledge contained in stock, derived from its form or content, or as objects implicitly defined by the choice of variables of statistical analysis. One example of the K-O view when applied is taken from the Finnish cargo handling company Cargotec and their transfer of the manufacturing solution of reach stackers (heavy forklifts) from their main and original manufacturing facility in Lidhult, Sweden to their Shanghai plant in 2005-2006. The strategy was to replicate the manufacturing set-up without (initially) adapting to local conditions. The products were designed to be dismantled, transported to China and re-assembled in Shanghai. On the individual level, the operators from Lidhult acted as teachers and informants for the Chinese operators who visited Lidhult to learn how to assemble the reach stackers.

Knowledge as something that is constructed in a social context and which cannot be separated from the context or the individual (or knowledge as a subjective contextual construction, K-SCC).

Sveiby states that this view is based on Polanyi's idea of personal knowledge (Polanyi, 1958). Among authors that subscribe to this view, Nonaka (Nonaka, 1994; Nonaka and Takeuchi, 1995) as well as Sveiby himself (Sveiby, 1997) can be highlighted. A theoretical concept that can be seen as connected to the K-SCC view is the term sensemaking (Weick, 1995).. This is seen as a process to understand the world. In Weick´s view it is an ―ongoing‖, social, retrospective process and it is dependent on the situation it is situated in (which is a construct in itself). The sensemaking process starts from a personal mental model of the world (see e.g. Klein 2008, Endsley 2000). In each situation a human actor is trying to figure out and understand the situation by comparing the situation with the mental model and important cues give an awareness of the situation. Endsley (2000) uses the concept of situation awareness to explain how perspectives of



situations can develop. He uses three different levels to explain situation awareness entailing at level 1 the perception of a situation, at level 2 the comprehension of the situation and at level 3 the projection of the situation. Level 1 is to focus perception on certain cues and events in the surroundings while level 2 discusses how information is combined, interpreted and retained and level 3 is a prognosis of possible future events with the current situation in mind. An example of the K-SCC view when applied can be taken from the Swedish home furnishing giant IKEA and a concept called ―Development on the Factory Floor‖. Here, the product and the manufacturing process are developed jointly by the R&D engineer and the manufacturing representatives at the local plant. The R&D engineer shares his/her thoughts and ideas about the prospective product with the manufacturing engineer, who in turn shares relevant knowledge about the possibilities and limitations of the manufacturing systems. These different views of knowledge seem to influence the choice of using KT or KS. In the literature presented in previous sections, there is a bias towards using KT if the author's view of knowledge tends towards K-O and a similar (if not as clear) bias towards KS if the K-SCC view is adopted. KS interpreted from a K-SCC view would contain aspects like trying to create meaningfulness for the participants and an increased importance of socialization (which are aspects similar to those included in the concept of sensemaking). Since all three terms are closely related, the different views of knowledge also influence the view of knowledge barriers and how to overcome them. If knowledge barriers are regarded as broken transfers it seems like the view coincides with a K-O view. In this view the solution to overcome barriers is to just see to that the knowledge is spread further on to the recipients. Knowledge is rather clear and straight forward in this view. Only when the knowledge is spread and noticed, the solution will be obvious and the knowledge barrier torn down. A knowledge barrier and a failure in knowledge transfer is more or less the same thing, just as that a failure in transfer of the knowledge will result in a knowledge barrier. Knowledge is definitely considered as some kind of object that can be easily moved in this view. Further, the distinction between information, data, and knowledge is not clear. These solutions are hardly possible if a K-SCC view is adopted. Here, knowledge cannot be taken out of context and treated as something to transport. Instead considerations of how the information will fit into the situation and be treated in making sense (in connection to the prevailing mental model) have to be done. When knowledge barriers are considered as lack of education the situation becomes somewhat more complicated. In a K-O view that kind of knowledge barriers can be lowered by standardized education about a topic. In a K-SCC view, the knowledge fit within the situation and with the recipients has to be considered. If Szulanski’s (2003) search for best practices is used as an example, the question would arise if all best practices can be interpreted in the same way in all situations. Considering knowledge as part of the perceptual system complicates the overcoming of knowledge barriers considerably. This view seems to be closest to a K-SCC view. Knowledge, in a sense, cannot be transferred but has to be redeveloped by each individual. New knowledge has to fit a mental model, be incorporated by sensemaking into this model and by that develop and change it. In this view there is no way to state what knowledge is because of the tight connection to earlier experiences and personal values and background. This means that a knowledge barrier cannot be overcome by just presenting knowledge to the individual by giving access or educate the person in a standardized course. There is a distinct difference between information and knowledge in this case where information is some kind of objective entity presented to the person that that individual might transform to knowledge by its sensemaking. To overcome such knowledge barrier several additional factors have to be considered. For KBs, it can be said that the original definition of KBs as a lack of knowledge (if you adopt the K-O view), or a lack of possibility to make sense of something (in line with the K-SCC view) becomes blurred and diluted by later contributions. Szulanski's term „lack of absorptive capacity’ is partly a lack of knowledge (in accordance with Attewell (1992)) but it also allows other influencing factors, such as intelligence or logic skills, to be included. Another of Szulanski's terms is „arduous relationship’. This factor is hardly connected to knowledge. Instead, it is clearly connected to interpersonal relations. In this sense makes these



different views also the term KB a bit blurry. Another problem with the diversion from the original definition can be exemplified by using Riege. He refers to different experience levels – which might be interpreted as different content in personal knowledge – as a KB (Riege, 2005). Later, he modifies this to ―resistance to sharing knowledge because of differences in experience levels‖ (Riege, 2007: 55) and suggests that this might be overcome by, for example, better integration. When doing this, he redefines the content of a KB from being lack of knowledge (in line with the second view of KBs) or as a part of a perceptual system to become a barrier to KT or KS (in line with the third view). If a K-SCC perspective is adopted, neither of Riege’s proposed suggestions would help to overcome differences in knowledge, but only to smoothen transfer of information. The following example is taken from a study conducted to explore the launch of an information and communication system for public safety organizations based on mobile phone technology. Here, it became quite obvious how many different types of KB interact in the same situation. In this study, user organizations and launching organizations were questioned regarding their impressions of the launch. The user organizations stated that it was problematic and that confidence in the system decreased when observing deficiencies in reception due to insufficient mobile network coverage. However, the specialists in the launching organization stated that they could not understand why that was a cause for concern – it was easily solved by installing a new radio base station. In this example, the traditional technical KB is present and it can easily be identified both from an objectivistic (K-O) view and a subjectivist (K-SCC) view. However from a K-SCC view, two other KBs can be identified: the lack of understanding from the launching organization can be interpreted as a KB related to how the user organizations are constituted. For the user organizations, KBs are not limited to the technical problem (the traditional view of KBs) but also to how the launch of the process to acquire a new base station is started.

5. Conclusions

After having reviewed literature in KM, we conclude that the three terms knowledge transfer, knowledge sharing and knowledge barriers are blurry. The blurriness is related mainly to which with what view and understanding of knowledge that it is used. Regarding use of the terms, there are clear indications that authors who use the term KT have a tendency towards the K-O perspective and that authors who use the term KS are drawn more towards the K-SCC perspective. The view of KBs and the interpretations of how to lower or pass a KB differ depending on the view. To find useful content in any definition, it is necessary adapt it to the specific situation. What effects would these two perspectives have on our blurry terms? One effect would definitely be how to manage the processes of KT and KS and KBs related to those processes. If you have a K-O perspective and want to create good conditions for knowledge flow, you amplify the enablers, suppress disabling conditions and overcome obstacles, including the barriers. In a K-SCC perspective, you focus more on the development of ―ba‖ (―ba‖ is a Japanese word that roughly translates as ―space‖ or ―environment‖ and it was introduced into the KM sphere by Nonaka and Konno (1998)), to better fit individuals who need to develop personal knowledge with the help of those who have already developed it. The authors of this article believe that the positive effect of KM will improve if a well thought out standpoint of practitioners and researchers would fit the type of problem and the ontological thoughts well. These standpoints needs to be considered also when, for example, IT-systems aimed at improving KM are developed so that functions and content match what is requested.

6. A final thought

Other key terms in KM are also likely to be affected by different perspectives. Even though it is not this paper's original focus, we cannot refrain from making the following comment: The concept "ba" (Nonaka and Konno, 1998) is also affected by the knowledge perspective. From a K-O perspective, "ba" has to do with designing the physical (or virtual) space to optimize KT or KS. However, from a K-SCC perspective "ba" has more to do with the "spacetime" (cf. Einstein, 1905) since the context changes over time and affects knowledge. In other words, a particular line of reasoning and logic built on knowledge seems straight if you have a similar background and experience (or frame of reference) to the individual who harbors the knowledge but appears more bent if you have a background and experience that differs significantly!



References

Allen, T. J. (1977) Managing the Flow of Technology, MIT Press, Cambridge, MA. Appleyard, M. M. (1996) „How does knowledge flow? Interfirm patterns in the semiconductor industry’, Strategic

Management Journal, Vol 17, No. pp 137-154. Argote, L. and Ingram, P. (2000) „Knowledge transfer: A basis for competitive advantage in firms’, Organizational

Behavior and Human Decision Processes, Vol 82, No. 1, pp 150-169. Attewell, P. (1992) „Technology Diffusion and Organizational Learning - the Case of Business Computing’,

Organization Science, Vol 3, No. 1, pp 1-19. Badaracco, J. L., Jr. (1991) „Alliances Speed Knowledge Transfer’, Planning Review, Vol 19, No. 2, pp 10-16. Bundred, S.. (2006), „Solutions to Silos: Joining Up Knowledge’, Public Money & Management, April 2006., pp

125 – 130. Cabrera, A. and Cabrera, E. F. (2002) „Knowledge-sharing dilemmas’, Organization Studies, Vol 23, No. 5, pp

687-710. Caldwell, L. K. (1967) „Managing the Scientific Super-Culture: The Task of Educational Preparation’, Public

Administration Review, Vol 27, No. 2, pp 128. Choo, C. W. and Alvarenga Neto, R. (2010) „Beyond the ba: managing enabling contexts in knowledge

organizations’, Journal of Knowledge Management, Vol 14, No. 4, pp 592-610. Clark, K. B. and Fujimoto, T. (1991) Product development performance: strategy, organization, and management

in the world auto industry, Harvard Business School Press, Boston, Mass. Dyer, J. H. and Nobeoka, K. (2000) „Creating and managing a high-performance knowledge-sharing network:

The Toyota case’, Strategic Management Journal, Vol 21, No. 3, pp 345-367. Easterby-Smith, M., Lyles, M. A. and Tsang, E. W. K. (2008) „Inter-organizational knowledge transfer: Current

themes and future prospects’, Journal of Management Studies, Vol 45, No. 4, pp 677-690. Einstein, A. (1905) ’Zur Elektrodynamik bewegter Körper’, Annalen der Physik, Vol 322, No. 10, pp 891-921. Endsley, M. R. (2000), „Theoretical underpinnings of situation awareness: a critical review’, in Endsley, M. R. and

Garland d. J. (eds), Situation awareness analysis and measurement, Lawrence Erlbaum Associates, Mahwah, NJ

Fernie, S., Green, S. D., Weller, S. J. and Newcombe, R. (2003) „Knowledge sharing: context, confusion and controversy"’, International Journal of Project Management, Vol 21, No. 3, pp 177-187.

Grant, R. M. (1996) „Toward a knowledge-based theory of the firm’, Strategic Management Journal, Vol 17, No. pp 109-122.

Gupta, A. K. and Govindarajan, V. (2000) „Knowledge flows within multinational corporations’, Strategic Management Journal, Vol 21, No. 4, pp 473-496.

Hansen, M. T. (1999) „The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits’, Administrative Science Quarterly, Vol 44, No. 1, pp 82-111.

Ipe, M. (2003) „Knowledge Sharing in Organizations: A Conceptual Framework’, Human Resource Development Review, Vol 2, No. 4, pp 337-359.

Jonsson, A. (2008) „A transnational perspective on knowledge sharing: lessons learned from IKEA’s entry into Russia, China and Japan’, The International Review of Retail, Distribution and Consumer Research, Vol 18, No. 1, pp 17-44.

Klein, G. (2008), „Naturalistic decision making’, Human factors, Vol 50, No. 3, pp 456-460. Kogut, B. and Zander, U. (1992) „Knowledge of the Firm, Combinative Capabilities, and the Replication of

Technology’, Organization Science, Vol 3, No. 3, pp 383-397. Liyanage, C., Elhag, T., Ballal, T. and Li, Q. P. (2009) „Knowledge communication and translation - a knowledge

transfer model’, Journal of Knowledge Management, Vol 13, No. 3, pp 118-131. Nonaka, I. (1991) „The Knowledge-Creating Company’, Harvard Business Review, Vol 69, No. 6, pp 96-104. Nonaka, I. and Konno, N. (1998) „The concept of "ba": Building a foundation for knowledge creation’, California

Management Review, Vol 40, No. 3, pp 40-54. Nonaka, I. and Takeuchi, H. (1995) The knowledge-creating company : how Japanese companies create the

dynamics of innovation, Oxford University Press, New York. Osterloh, M. and Frey, B. S. (2000) „Motivation, knowledge transfer, and organizational forms’, Organization

Science, Vol 11, No. 5, pp 538-550. Paulin, D. (2002) Virtual verification: Impact of a new work method on the final verification process, Licentiate

thesis, Chalmers University of Technology, Göteborg. Paulin, D. (2006) „The Effects on Knowledge Creation and Transfer in Production Process Verification due to

Virtual Prototypes’, [online], Academic Conferences Ltd, www.ejkm.com Polanyi, M. (1958) Personal knowledge; towards a post-critical philosophy, University of Chicago Press, Chicago, Ramaswami, S. N. and Yang, Y. (1990) „Perceived Barriers to Exporting and Export Assistance Requirements’,

International Perspectives on Trade Promotion and Assistance, Vol No. pp 187-206 Riege, A. (2005) „Three-dozen knowledge-sharing barriers managers must consider’, Journal of Knowledge

Management, Vol 9, No. 3, pp 18-35. Riege, A. (2007) „Actions to overcome knowledge transfer barriers in MNCs’, Journal of Knowledge Management,

Vol 11, No. 1, pp 48-67. Sambamurthy, V. and Subramani, M. (2005) Introduction to: „SPECIAL ISSUE ON INFORMATION

TECHNOLOGIES AND KNOWLEDGE MANAGEMENT’, MIS Quarterly, Vol 29, No. 1, pp 1-7.



Sæmundsson, R.J. and Holmén, M. (2007) „Changes in Entrepreneurial Opportunities: New Capital Goods and Changes in Knowledge-Barriers to Entry’, [online], Institute for Management of Innovation and Technology, http://www.imit.se/pdf/reports/2007_146.pdf.

Schlegelmilch, B. B. and Chini, T. C. (2003) „Knowledge transfer between marketing functions in multinational companies: a conceptual model’, International Business Review, Vol 12, No. 2, pp 215-232.

Schwartz, D. G. (2006) Encyclopedia of Knowledge Management, IGI Global. [electronic], http://library.books24x7.com/toc.asp?bookid=14700.

Suneson, K. and Heldal, I. (2010) „Knowledge Barriers When Launching new Telecommunications for Public Safety’, In proceedings from The 7th International Conference on Intellectual Capital, Knowledge Management & Organisational Learning, The Hong Kong Polytechnic University, Hong Kong, China, 11-12 November 2010, pp 429-438.

Sveiby, K. E. (1997) The new organizational wealth : managing & measuring knowledge-based assets, Berrett-Koehler Publishers, San Francisco, CA.

Sveiby, K. E. (2007) „Disabling the context for knowledge work: the role of managers' behaviours’, Management Decision, Vol 45, No. 10, pp 1636-1655.

Szulanski, G. (1996) „Exploring Internal Stickiness: Impediments to the Transfer of Best Practice Within the Firm’, Strategic Management Journal, Vol 17, No. Winter Special Issue, pp 27-43.

Szulanski, G. (2003) Sticky knowledge: barriers to knowing in the firm, Sage Publications, London. Tanriverdi, H. and Iacono, C. S. (1999) „Diffusion of telemedicine: A knowledge barrier perspective’, Telemedicine

Journal, Vol 5, No. 3, pp 223-244. Tsai, W. P. (2001) „Knowledge transfer in intraorganizational networks: Effects of network position and absorptive

capacity on business unit innovation and performance’, Academy of Management Journal, Vol 44, No. 5, pp 996-1004.

van Wijk, R., Jansen, J. J. P. and Lyles, M. A. (2008) „Inter- and Intra-Organizational Knowledge Transfer: A Meta-Analytic Review and Assessment of its Antecedents and Consequences’, Journal of Management Studies, Vol 45, No. 4, pp 830-853.

Wang, S. and Noe, R. A. (2010) „Knowledge sharing: A review and directions for future research’, Human Resource Management Review, Vol 20, No. 2, pp 115-131.

Weick, K. (1995) Sensemaking in organizations, Sage Publications, Thousand Oaks, California, USA. Venkatesh, M., Nosovitch, J. and Khan, S. (1999) „Knowledge barriers to technology adoption: The case of

second-generation community networking’, [online], http://www.scn.org/the_network/Proj/ws99/venkatesh-et-al-pp.html.

Zielinski, J. G. (1962) „An attempt to construct a realistic theory of socialist economy’, Economics of planning, Vol 2, No. 1, pp 87-104.

ISSN 1479-4411 92 ©Academic Publishing International Ltd Reference this paper as: Pawlowski, J and Bick, M. ―The Global Knowledge Management Framework: Towards a Theory for Knowledge Management in Globally Distributed Settings‖ The

Electronic Journal of Knowledge

Management Volume 10 Issue 1 (pp92-108 available online at www.ejkm.com

The Global Knowledge Management Framework: Towards a Theory for Knowledge Management in Globally Distributed Settings

Jan Pawlowski1 and Markus Bick2 1Global Information Systems, University of Jyväskylä, Finland 2Business Information Systems, ESCP Europe Berlin, Germany [email protected] [email protected] Abstract: Our paper introduces the Global Knowledge Management Framework (GKMF) which describes components and influence factors of knowledge management in globally distributed settings. The framework identifies the key aspects when designing knowledge management processes and systems and can be used for two main purposes. On the one hand, it guides development processes by providing a solution space and success factors for decision makers as well as implementers. On the other hand, it is a reference for researchers to compare research in the field by providing a common set of context descriptions as well as aspects influencing the success of knowledge management solutions. We illustrate the application of our framework first within two scenarios and describe its first evaluation as a proof-of-concept in an educational setting. By that, we give insights into further research and development of the framework trying to stimulate discussion and initiating a broad initiative working towards global knowledge management. Keywords: global knowledge management, internationalization, global knowledge management framework, knowledge management processes, culture, knowledge management theory, process management

1. Introduction

In this paper, we introduce the Global Knowledge Management Framework (GKMF) which is a model to structure and compare influence factors on knowledge management (KM) in global settings. It serves as a guideline for researchers and practitioners to design, compare, and validate knowledge management systems based on a thorough analysis of current research of influence factors for successful KM around the globe. The framework describes components of global knowledge management settings and identifies the key relations and success factors. It is the first step towards a holistic theory in the domain. Knowledge management becomes more and more important in global settings (cf. Desouza & Evaristo, 2003, Holden, 2002). The influence of aspects like geographical dispersion, communication across time zones as well as cultural influence factors has become a focus issue in research for the past decade. A variety of topics has come up in the field to understand global knowledge management, focusing on foundational issues, KM implementation and adoption processes as well as specific issues in these processes, such as supporting single tasks or using certain interventions (cf. Alavi & Leidner, 2001). However, recent studies show that still a lot of KM projects fail (cf. Coakes et al, 2010) and not all influence factors are clearly understood. This is in particular the case for global settings in which the context plays a major role such as cultural (Holden, 2001, Pauleen, 2006), political, legal, or infrastructural aspects (Richter & Pawlowski, 2007). It is necessary to map current research to corresponding context information in order to make project and research results comparable and validate transferability across different contexts and cultures. In particular, the context of research projects as well as implementation and adoption processes should be captured in a clear way. By making KM project results comparable and mapping results and context, we will achieve a better understanding of what works in which organizational or cultural context. Frameworks define the relevant objects and their coherences as well as providing a scaffold for aspects that have to be considered during the design and implementation process. By that, frameworks are a proper solution to map the different contextual aspects, influence factors as well as results. We understand our framework as a conceptual model on the way to a holistic theory of global knowledge management identifying influence factors and interdependencies.





In the following, we derive such a framework for global knowledge management research and practice. We start with conceptual foundations and methodological considerations. We then introduce our Global Knowledge Management Framework (GKMF) and its application within a case study serving as a proof-of-concept. We conclude with recommendations for future research in the field of global knowledge management.

2. Related work

Frameworks describe concepts, aspects, such as processes or systems as well as their relations of a certain domain or problem to create a better understanding or to support specific purposes. Often, the concepts of reference models or architectures are used in a similar way. Reference models serve as conceptual models and – with a more practical orientation –blueprints for IS design (Fettke & Loos, 2003b) identifying the main components of design tasks for certain domains. As there is no clear definition of frameworks, the focus of frameworks and reference models might overlap and needs to be made explicit. In many domains such as software development, frameworks are used to understand the relation between components (such as program modules) and to structure and guide through a problem domain. We understand the framework as a step towards building a theory for global knowledge management understanding for example cultural and contextual influence factors which has not been achieved yet. The global knowledge management framework aims at describing and relating main components influencing KM design and adoption. Frameworks and Models for Knowledge Management In the domain of knowledge management, frameworks and corresponding approaches (architectures, models, reference models) are widely used to describe components, design aspects or technical architectures and their interdependencies (cf. Hahn & Subramani, 2000, CEN, 2004, Heisig, 2009). In many cases, KM frameworks are created to achieve a common understanding the domain (Bhagat et al, 2002, CEN, 2004, Maier, 2007), to structure approaches and practices (Grover & Davenport, 2001) and to identify research gaps (Alavi & Leidner, 2001, Grover & Davenport, 2001). Heisig (2009) analyzed around 160 frameworks to identify the success factors and most important components. The following aspects are identified as critical success factors: 1.) human-oriented factors (culture, people, leadership), 2.) organization (processes and structures), 3.) technology (infrastructure and applications) and 4.) management (strategy, goals and measurement) (Heisig, 2009 – see below). Within this paper, we identify some of the key aspects for KM research and development. From a globalization perspective, our analysis shows the importance of aspects which are affected when working in globally distributed settings such as cultural influences. Surprisingly, most of those frameworks do not cover global aspects – typical aspects which need to be taken into account on top of intra-organizational domestic KM projects are for example: inter-organizational processes and collaboration, communication processes, work in distributed teams, as well as additional barriers, new type of tools or instruments, or which knowledge to share in different organizational models (cf. Holden, 2002, Desouza & Evaristo, 2003, Prikladnicki, Audy, Evaristo, 2003). Thus as a first step, it is necessary to analyze which current models can be used as a basis building a framework for global purposes. Within this paper, we analyze two frameworks as an example to illustrate the structure and usage of frameworks. One of the main frameworks currently used in practice is the framework by CEN (2004) created in the European standardization community. It provides a common terminology and frame of reference for organizations involved in knowledge management (Figure 1).

Jan Pawlowski and Markus Bick


Figure 1: Knowledge Management Framework (CEN, 2004)

Figure 2: Knowledge Management Architecture (Maier, 2007



The CEN framework shows a clear process orientation, aiming at describing core business processes as well as knowledge-related processes. It extends those processes by enablers: knowledge capabilities on an organizational (e.g., vision, strategy) and individual level (such as skills, competences, methods, tools). This framework has created a common terminology and structure as well as guidelines around those. However, it does not cover the main aspects of globally distributed KM but provides extension options, such as extending processes or adding enablers and additional components. It also does not incorporate the research perspectives (e.g., aspects studied or models validated). However, due to its relevance to practice, it is a good candidate to be used as a basis for a global framework. As second framework, we consider the KM architecture by Maier (2007). This framework is organized on different levels (strategic, design, organizational) and by knowledge types which are connected by generic knowledge activities. The architecture identifies key aspects of knowledge management as well as potential tools and methods around those (e.g., ontologies, technical architectures, or roles). It is based on clear, research-based classifications and categorizations and identifies influence factors and solutions for different purposes. Thus, it is applicable for structuring both research and practice approaches. However, the framework also needs to be extended regarding the specifics of globally distributed KM activities. The illustrated frameworks as well as most of the current frameworks (Heisig, 2009) do not – and do not intend to – cover global aspects. Thus, it is necessary to identify how global knowledge management is different from domestic, intra-organizational knowledge management. Our brief analysis has shown that suitable, extensible frameworks exist but they need to be extended regarding global processes. Global Knowledge Management As global knowledge management we understand KM activities performed in globally distributed intra- or inter-organizational settings. In such settings, KM design, acceptance and deployment are influenced by a variety of additional aspects, in particular cultural aspects (both organizational and ethnic regional / national culture) (DeLong & Fahey, 2000, Holden, 2002, Alavi & Leidner, 2001). We have analyzed culture models and practices regarding KM-related aspects and specific characteristics. Knowledge management in a broad sense is a critical aspect of globally distributed work processes (cf. Holden, 2001, Holden, 2002). However, there are certain specific questions which extend domestic intra-organizational processes. These need to cover processes as well as strategies between distributed organizations. They need to take global knowledge exchange and distribution into account. This leads to a variety of additional influence factors, barriers and challenges in global settings (cf. Holden, 2002, Desouza & Evaristo, 2003, Prikladnicki, Audy, Evaristo, 2003, Sangwan et al., 2003) – examples for this are culture-specific factors, communication factors, additional individual and organizational competences as well as further requirements towards (internationally usable) tools. Following Heisig’s (2009) analysis struacture regarding KM success, it is clear that a global environment brings up new challenges:

Human-oriented factors (culture, people, and leadership): Human work as well as collaboration and communication behavior is based on culture (both organizational and ethnic such as regional / national culture). Thus, typical KM activities like knowledge sharing are strongly influenced.

Organization (processes and structures): Organizational processes also differ depending on organizational and geographic culture. Obviously, it is necessary to coordinate KM processes in distributed organizations and between organizations with different organizational and ethnic culture.

Technology (infrastructure and applications): Technology infrastructures also differ in different countries. The acceptance of applications is also dependent on preferences (e.g., how technologies are accepted, which social networks are preferred in a country)

Management (strategy, goals and measurement): Management practices differ also depending on ethnic and organizational culture. Thus, it is necessary to align KM strategies as well as corresponding management processes.



Our brief introduction of global challenges shows that cultural influences change the requirements and practices of KM in globally distributed settings. Hence, it is the challenge of a new (globally oriented) framework to capture these key influence factors and relate them to the main components of KM projects.

3. Global Knowledge Management Framework

In the following, we describe the Global Knowledge Management Framework (GKMF) providing a reference for structuring research and practice projects as well as guiding adopters through implementation and deployment process. The main objective of the framework is to identify global aspects of KM projects and interdependencies between the components. As a conceptual model, it is a first step towards a theory of global knowledge management. Methodology KM is highly dependent on the context and cannot be validated separated from practical implementations. It is one of the main objectives of KM research to construct solutions which achieve practical impact and benefits as the main goal. Therefore, our framework is built as a design science research approach (Hevner et al., 2004) – based on a thorough literature analysis of frameworks and global influence factors, we have identified gaps and extension needs to create this new artifact. The framework is initially validated in a first case study (Yin, 2003) in an educational setting as proof-of-concept. By this, we aim at progressing from a conceptual framework towards theory building for knowledge management settings. Framework Construction The main goal of our GKMF is to identify and relate global influence factors for distributed knowledge management projects in global settings. It aims at providing a base for research (as an analysis tool) and practice (as a guideline for development). We base our development on a combination of frameworks (Bhagat et al., 2000, CEN, 2004, Maier, 2007, Heisig, 2009) and an analysis of influence factors, barriers and challenges in global settings (cf. Holden, 2002, Desouza & Evaristo, 2003, Prikladnicki, Audy, Evaristo, 2003, Sangwan et al., 2003). In a first step, we have identified commonalities of the diverse frameworks (strategies, processes, knowledge resources, tools) and harmonized the different terminologies. As a second step, extensions were derived and mapped to the initial components of the framework. The components were continuously revised during the literature analysis. Another issue is the representation of the framework. Many models remain conceptual and do not provide a detailed description of the components. Thus, we have developed a description format which contains descriptive attributes (e.g., to describe cultural aspects). This information model can be used to specify instantiations of the components. As an example, we represent assessment aspects for KM – the attributes in our model can thus be used to create concrete assessments to validate KM projects. A particular focus is the identification of relations and inter-dependencies between the components – this is in particular important for understanding the mechanisms and impact (e.g., which intervention has positive impact on which metrics for which process?). This distinguishes our framework clearly from other frameworks which just specify components. The complete framework consists of the following components (Figure 3):



Context

Culture

Str

ate

gie

sIn

fras

truc

ture

s

Ins

trum

en

ts

Hu

ma

n-b

ase

d

instru

me

nts

Tech

no

log

ies a

nd

too

ls

Kn

ow

led

ge

Pro

ble

ms

Re

sou

rce

s

Results

Performance Knowledge …

Processes

Intervention A Intervention B Intervention N

Validation, Feedback, Improvement

External Processes

Business Processes

Knowledge

Processes

Stakeholders

Society Organization Individual

embedded in

Improved by

performruns

change

use

inf luences

Support

Measuredby

Measuredby

enable

guide

inf luences

create

measures

inf luences

inf luences

enable

Figure 3: Global Knowledge Management Framework

In the following, we describe the components in detail – we focus not solely on identifying those components but provide potential instantiations as well as main relations. As an example, potential instruments are listed as design alternatives. By this, the framework also creates a solution space for global knowledge management design. It should be noted that the following description of the components is linear due to readability – the framework itself is a networked model (Figure 3) with many interconnections and relationships between the components. These are described in the textual illustration. Processes The core of the framework is described by processes on three levels. Business processes denote the core processes of an organization such as teaching for educational organizations or software development and deployment for software businesses. It is not possible to provide a generic process scheme for all domain – thus, we propose an abstract generic scheme such as ebXML (UN/CEFACT & OASIS, 2001) and extend this by domain specific processes (cf. Fettke & Loos, 2003b). The core business processes are supported by embedded knowledge processes which enable knowledge management within and outside the organization, e.g., knowledge identification, knowledge sharing or knowledge distribution (Probst, Raub, Romhardt, 1999). In the global context, those processes (e.g., negotiations, cooperation agreement, or coordination of distributed development) are highly related to external processes with stakeholders who are distributed across the globe. These processes are accompanied by interventions and supporting processes (e.g., awareness building or change processes) which are accompanying processes to improve knowledge management as well as validation processes measuring the success of the interventions (cf. Maier and Remus, 2003). The following table summarizes the main aspects of the process component: For each process class, we have also identified possible solutions (e.g., business processes can be specified using ebXML as a template).



Table 1: Process categories

Process Description Sample Values /

Attributes Source / References

Business Process

Core business processes of an organization

Procurement Human resources

Transportation Manufacturing

Marketing & sales Customer service

Domain specific process, e.g., healthcare,

education

UN / CEFACT & OASIS (2001), Fettke & Loos

(2003b), ISO/IEC (2005)

Knowledge Processes

Knowledge related activities of the organization

Knowledge identification Knowledge acquisition

Knowledge development Knowledge

distribution/sharing Knowledge preservation

Knowledge use

Probst, Raub, Romhardt (1999)

CEN (2004) Maier (2007) Heisig (2009)

External Processes

Processes with external stakeholders (cooperation

partners, strategic alliances, customers, offshore partners)

Cooperation establishment

Awareness building Negotiation

Cooperation agreement Culture exchange

Pirkkalainen et al., 2010

The above process specifications show sample processes which can be addressed and which might be modeled – based on those process specifications, it is necessary to identify which processes are required in a specific setting and how those processes are embedded (Remus & Schub, 2003). The embedding is one of the critical success factors: it is highly necessary to have knowledge processes as an integral part of core business as well as external processes. Furthermore, new interventions need to be embedded seamlessly as well. The process design has a main impact on acceptance, performance as well as speed and quality of knowledge creation. Stakeholders and context The categories stakeholders and context are discussed in one paragraph as there are several overlaps. The organizational background is – in some research works – seen as context, in other works the organizational aspects are designed and changed (e.g., organizational culture). In particular, barriers are overlapping. In our context, some barriers are caused by the cultural background but are observed when studying individuals. Thus, it is useful to combine those categories. The category stakeholders describes characteristics of participating stakeholders. This can be related to individuals (e.g., preferences, interests), organizations or societies. The stakeholder category is in most research works an important factor. In many cases it is seen as a constraint as research investigations are done for certain target groups or types of organizations. Whereas these characteristics are mostly part of the context, other aspects are subject to research analyses, in particular barriers (individual as well as organizational). The sub-category context describes the context or environment in which knowledge management takes place. In most cases, it relates to organizations (organizational culture, strategies, cf. Desouza & Evaristo, 2003) or society (ethnic culture, technological infrastructures, policies, see Richter & Pawlowski, 2008). A focus in this category is the analysis of cultural aspects influencing communication, collaboration and coordination of knowledge processes (DeLong & Fahey, 2000, Pauleen, 2006). The following table summarizes our findings regarding the categories of contents and stakeholders.



Table 2: Stakeholder and context categories

Category Description Sample Values / Attributes Source /

References

Individual: Personal

Characteristics

Description of individuals’

characteristics

Demographic data (name, age, gender, …) Qualifications

Competences (Skills, Knowledge, Attitudes) Globalization competences

Educational preferences Media and application preferences

Cultural experiences, offshore assignments

Maier & Thalmann (2010), Pawlowski

et al. (2010) Stewart (1998)

Individual: Barriers

Potential barriers towards knowledge

management utilization

Lack of time Fear about job security;

Lack of awareness for KM Use of strong hierarchy, position-based status

Lack of time and interaction Poor verbal/written communication and

interpersonal skills; Age, gender, cultural differences;

Lack of networking skills Lack of trust

Riege (2006), CEN (2004)

Maier (2007). Argyris (1990) Bick (2004),

Fahey & Prusak (1998)

Lugger & Kraus (2001)

Szulanski (1996), Holsapple & Joshi

(2000)

Context: Organizational Characteristics

Description of organization

characteristics

Name Size

Type (private, government, NGO, …) Sector (healthcare, automotive, …)

Vision & Strategy for KM Core organizational capabilities

Desouza & Evaristo (2003),

Earl (2001)

Context: Organizational

Barriers

Potential organizational

barriers towards knowledge

management utilization

Lack of leadership and managerial direction Shortage of formal and informal spaces to

share, reflect and generate (new) knowledge; Lack of a transparent rewards and recognition

Insufficient corporate culture Shortage of appropriate infrastructure

supporting sharing practices Deficiency of company resources

Communication and knowledge flows are restricted

Physical work environment and layout of work areas

Internal competitiveness within business units,

McDermott & O’Dell (2001), Riege (2006), CEN (2004)

Maier (2007). Argyris (1990) Bick (2004),

March & Olsen (1976)


Context: Cultural Characteristics

Description of cultural

characteristics

Power Distance, Uncertainty avoidance, individualism/collectivism, … Value of errors and failures Roles of knowledge experts

Value / direction of knowledge sharing Understanding of contextual knowledge Understanding of common knowledge

Ways of decision making and negotiation

Richter & Pawlowski (2008) Bick & Pawlowski

(2009)

Context: Cultural Barriers

Potential cultural barriers towards

knowledge management

utilization

Inability of communication and collaboration Fear / insecurity

Lack of awareness and sensitivity Lack of integration skill / will

Language issues Fear of imitation

Desouza & Evaoristo (2003), Holden (2002), McDermott & O’Dell (2001), Kalkan (2008), Pauleen (2006)

Context: Infrastructure

Characteristics

Description of infrastructure

National ICT policies Strategies

Communication networks. network availability Security / privacy regulations and perception

National initiatives (libraries, services)

Gibbs et al. (2002) Richter &

Pawlowski (2008) Bick & Pawlowski

(2009)



Technology / media acceptance


References Context: Success

factors Success factors for KM in organizations

Integrated Technical Infrastructure Knowledge Strategy that identifies users,

sources, processes, storage strategy, knowledge

Clear knowledge structure Motivation and Commitment

Organizational culture supporting sharing and use of knowledge

Senior Management support including allocation of resources, leadership, and

providing training Measures are established to assess the

impacts Clear goal and purpose for the KMS

Search, retrieval, and visualization functions Work processes incorporate knowledge

capture and use Learning Organization

Security/protection of knowledge

Maier (2007), Bick (2004),


Davenport & Prusak (1998) Lehner & Haas

(2010)

The table above shows samples for the context and stakeholder view. In contrast to domestic intra-organizational knowledge management frameworks, these categories contain the main extensions and particularities for global settings: cultural aspects, contextual influence factors and corresponding barriers. The components have a strong impact on further framework components – as an example, it is clearly necessary to include dedicated awareness building and training processes into the knowledge processes to facilitate cultural understanding. Cultural factors also influence how and which knowledge is shared. This can be expressed by metrics such as the amount of knowledge elements shared or the communication intensity between stakeholders. Knowledge This component describes and characterizes knowledge aspects and elements which are shared or required in the organization. This category contains for example problems to which knowledge is applied as well as resources representing codification of knowledge.

Table 3: Knowledge categories


References

Knowledge element

Description of knowledge areas of

an organization

Subject area Type

Representation / codification Culture specifics (common, contextualized, …)

Thalmann (2011), Pirkkalainen et al.

(2010)

Knowledge type What kind of knowledge

Knowing that / knowing how Tacit / implicit / explicit

Knowledge as object / knowledge as process Importance (routine, important, critical) Complexity (simple, expert, specialized)

Group (team, organization, strategic partners, …)

Ryle (1949), Polanyi (1966)

Nonaka & Tackeuchi (1995)

Hansen et al (1999)

Problem Problems to which

knowledge is applied

Problem description Context

Related knowledge, competences, actors Kalz et al. (2010)



This category describes the core of knowledge management systems – it shows only one perspective on knowledge with the assumption that knowledge is intended for problem solutions. However, it needs to cover both, human- as well as technology-oriented knowledge aspects (i.e. attached to actors or represented in a codified form). One main influence is the question which and how knowledge is made explicit: In highly contextualized cultures, less knowledge is made explicit. Also common knowledge is perceived differently. Thus, a strong relation to cultural aspects exists. Furthermore, interventions need to be tailored to the types of knowledge shared and problems addressed. As an example, highly complex knowledge innovations can only be achieved using certain interventions such as focused think-tanks, open spaces or using creativity tools. Instruments and interventions Instruments describe methods and activities to realize the knowledge processes. The main categories (Maier, 2007) are human-oriented instruments (e.g., job rotation or knowledge fairs) and technological instrument (e.g. knowledge bases or communication tools). These interventions need to be embedded in the above described process areas (Maier and Remus, 2003). The following table shows a (small) subset of potential interventions and instruments.

Table 4: Instrument categories


References

Human-based instruments

Description of the instrument

Mentoring Open Space

Job Rotation, Job Enlargement Career Planning

Team Development Simulation Games

Future Search Conference

Maier (2007), CEN (2004), Bick (2004)

Technology-based

instruments

Problems to which knowledge is applied

Document / Content Management Micro-Blogging

Search, Browse, White Pages Data Mining

Videoconference, Messaging Mash Ups

News-Channel / News-Feed Application Sharing

Social Networks

Maier (2007), CEN (2004), Bick (2004),

Mentzas et al. (2002)

The list of instruments is of course a small set of options as this is a main research field of constructive research in information systems, human resource management (HRM) and related areas. However, a focus is the usage and validation of instruments to address certain barriers. Results Results describe the key outcomes of the knowledge processes using some form of assessment and metrics (Bose, 2004). Obviously, there are many approaches to assessing and validating the success of KM activities (cf. Grossmann, 2005). The assessment can incorporate a variety of aspects: from a project management perspective, the project success needs to be validated. From a knowledge perspective, it is important to assess newly generated or utilized knowledge as well as measurements of the knowledge and its impact (Shin, 2004). Measuring knowledge management success can be in principal done on a general level (e.g. using the Information System Success Model, Kulkarni et al, 2006, Jennex and Olfman, 2005, 2006, Lindsey, 2002) or for specific components such as organizational capabilities (Gold et al., 2006), performance (Massey, 2002, Lee et al, 2005) or knowledge / competence development. A starting point for comprehensive metrics are the reviews by Bose (2004) and Kankanhalli & Tan (2005) which identify comprehensive categories and aspects of metrics on an organizational level. However, the measurement of global aspects is in many cases only addressed indirectly. We have thus derived initial assessment factors through barriers and success factors (e.g., measuring communication



intensity as a metric for addressing the potential loss of communication richness or needed interventions / escalations to avoid project failure).

Table 5: Result categories


References

Knowledge Measurement of

knowledge and core processes

Acceptance of knowledge management systems (KMS)

Usability / usefulness of KMS Knowledge assets (number, usefulness,

complexity, …) Knowledge sharing (number of knowledge

elements, motivation, know Knowledge utilization (usage of knowledge

elements, number of users per element, perceived usefulness, …)

Kankanhalli (2005),

Lee (2005) Maier (2007)

KM Project success

Success of specific KM projects

Project awareness and commitment Project usefulness KM effectiveness

KM process capabilities KM infrastructure capabilities

Job performance

Jennex & Olfmann (2004)

Intellectual capital

General knowledge-related metrics of an

organization

Human capital / knowledge development (no. of employees, employee turnover, profits /

employee, motivation, satisfaction, …) Customer benefits (rating, sales / customer, satisfaction, length of customer relationship,

response time, …) Structural capital (expense / revenues, errors /

order, quality performance, …) Financial focus (assets / employee, revenues

per new business operation, value added / employee, return on education, …)

Process improvement (process timing, knowledge process time / total process time,

…) Innovation (number of patents, improvement of

product renewal, …)

Bose (2004), Maier (2007)

Stewart (1998)

Global aspects Measuring

international aspects

Strategic partnerships / collaborations Communication intensity

Coordination activities, coordination breakdowns

Escalation procedures Management meetings

Improvement of global competences Cultural awareness and sensitivity

Team understanding, team awareness Imitations

DeLong & Fahey (2000), Desouza & Evaoristo (2003), Holden (2002), Kalkan (2008)

This category is related to other components in all types of research works. In case that success models are used, this is modeled in structural equations, in other cases the relations are implicitly described in publications. For global settings, these metrics are applied in a similar way as traditional KM assessment (cf. Cummings, 2004) in which mainly the influence factors are analyzed. However, there are very few dedicated publications on measuring the specific global effects of knowledge management – most publications address specific aspects such as the communication and team work in global settings. Finding appropriate, explanatory, comprehensive metrics for knowledge management in global settings beyond performance and effectiveness is still a challenge. Main relations of the components



One of the key tasks for researchers and practitioners using the GKMF is to identify the relations between its components. Due to space limitations we cannot mention every single relation but the main relations. From a research perspective, it can be stated that the success of knowledge management projects is not generalizable and not necessarily predictable as it depends strongly on the context (Jennex, 2008). Thus, it is necessary to map and understand more and more relations for different contexts. However, the main relations can be identified from existing qualitative (Heisig, 2009) and quantitative (Jennex & Smolnik, 2008) research. We distinguish between general relations (for all KM projects) and specific globally oriented relations (mainly applicable in global settings). The first relations are applicable for most KM projects:

Context – Processes / Interventions: The success of KM projects depends highly on the organizations’ context as the organizational context influences strongly initial barriers. Furthermore, potential instruments depend on the context, i.e., depending on the organizations’ processes and infrastructure, different technology options need to be chosen.

Processes – Interventions: In global settings, processes are organized differently (different ways of working, different roles and responsibilities). To develop successful KM projects, processes of different organizations need to be understood and aligned, interventions need to be integrated in their process models.

Strategy and Management – Processes: The support and importance of KM in an organization’s strategy is a clear requirement for KM success. In global settings, strategies of multiple partners need to be aligned and implemented in common processes. This means that business processes are affected, e.g., by adding change and integration processes.

Instruments / Interventions – Processes: Chosen interventions influence the success of a KM project. The balanced combination and related (change and awareness) activities influence how business processes incorporate KM and how knowledge is utilized.

The following relations are in particular important for globally distributed KM settings:

Culture – Knowledge / Processes / Interventions: In global settings, both organizational and ethnic culture have strong influences. Culture influences how processes are managed and performed, how knowledge is shared and communicated, how technologies and interventions are perceived. When different cultures are involved, additional (and totally different) interventions need to be applied.

Barriers – Processes / Interventions: A variety of barriers exist in KM projects – for global settings, these barriers need to be addressed by different interventions (and thus different processes).

Knowledge – Interventions: Different types of knowledge are handled differently across cultures. Depending on the knowledge types, interventions are chosen and selected. In particular, this is relevant in global settings to make common knowledge explicit and externalize it.

In this section, we have briefly illustrated main relations for global settings. This description serves as a starting point for further research as the GKMF is intended to provide a structure for comparative research as one of its main goals.

4. Application scenarios for the GKMF

We have shown the variety of potential components, attributes and instantiations of the Global Knowledge Management Framework. It thus serves as an initial solution space for global KM. Describing the above mentioned categories, elements, and relations enables us to compare both research works as well as implementations. Thus, the framework serves also as a basis for comparing current and future research with a focus on the global context. In the following, we describe how the GKMF could be applied with two short scenarios. According to Hevner et al. (2004) scenarios could be applied as evaluation technique for innovative artifacts, in particular for new and complex artifacts which cannot be evaluated as such in one step. How to describe and analyze research models based on the framework A first scenario is using the framework for building research models leading to a theory of global KM. For example a variety of models have been developed to analyze the success of knowledge management



(Lehner & Haas, 2010, Kulkarni et al., 2006, Jennex & Olfman, 2005, 2006), some of them addressing culture as a key aspect (Leidner et al, 2006, De Long & Fahey, 2000) or variable in a structural equation model (Lindsey, 2002, Jennex & Olfman, 2004, Urbach at al., 2009). For this research scenario, the GKMF provides a description format for:

Describing the context of the research setting: The context of research can be clearly and transparently described by using the context component of the GKMF. This enables researchers to better describe their own settings and moreover creating a basis for comparative research. It also allows analyzing the transferability of research works.

Development of research models: GKMF provides main influence factors on a detailed level. These can be initially used for building quantitative research models (e.g., barriers or cultural factors as exogenous variables, result attributes as endogenous variables).

Building domain specific frameworks: The model can also be used as a basis for more specialized models (e.g., KM for health care in developing countries). For this, we provide a common base of existing knowledge.

Therefore, the framework can be used as a starting point or artifact for transparent research towards better understanding of global KM. Guiding the KM design process The GKMF can also be used to guide international KM design and development processes. These processes need a clear planning of knowledge management activities as those are crucial for success in inter-organizational, regionally / geographically distributed processes. Thus, the following steps can be derived from the model:

Identifying the context and barriers of stakeholders: In an initial phase, stakeholders across different organizational units and partner organizations are asked about their KM context and barriers towards using and providing KM resources. The framework is then used to identify potential (cultural) barriers towards knowledge sharing. As an example, questionnaires can be directly created based on the GKMF attributes and thus provide a guideline for the requirement analysis.

Designing knowledge sharing processes: Based on the knowledge process component, a set of processes and activities for knowledge sharing as well as cultural preparations are planned and implemented, taking guidance on process embedding into account (in particular for employees, additional activities need to be embedded into their everyday routine). Thus, the GKMF knowledge processes serve as guidance to take different phases into account and to connect them to basic work processes.

Providing a supporting infrastructure: Based on the barriers, supporting interventions and tools are planned. Based on barriers and context, tools and accompanying processes are selected to 1.) overcome barriers, 2.) support the combination of business and knowledge processes, and 3.) address culture-specific issues.

Assessing the success of the project: The success of scenarios / KM projects is essential. KM projects need to show clear evidence that continuous improvements are achieved. For this, indicators can be derived from the knowledge-focused indicators of the GKMF.

As a conclusion, the model serves as a guideline which provides a solution space but not the solution itself. In particular, the provision of barriers, success factors, and inherent recommendations (e.g., process embedding or analysis references) is the main added value of the framework. Case Study: GKMF for KM Design Education In the following, we briefly describe an initial case study carried out in an international educational setting as proof-of–concept of the GKMF. The framework was applied by an international group of students during their final assignment of a summer school course on Global Knowledge Management at the University of Jyväskylä (Bick, Pawlowski, Lehner 2011). Most of the students applied for this specific knowledge management course after a) taking part in a introductory KM course at their home universities



or b) participating in the general KM course held during the 21st Jyväskylä summer school (n.a. 2011). Therefore, the participants can be seen as experienced in the field of KM aiming – as an international group – at additional insights into culture, context etc. After the course with lessons on cultural models or integrated (global) knowledge processes the students had to prepare a final assignment to achieve the corresponding credits. The assignment was an extension of the Securitech Ltd. case study (Eppler 2003). This case study was extended with regard to an internationalization strategy by Securitech in general and to China in particular and coping with corresponding global knowledge management issues. This scenario was chosen as the GKMF should be understandable for managers and professionals with a basic knowledge in KM who need to address global issues and design and execute complex projects. Students on this level are thus a fitting target group using the GKMF for both, professional and educational purposes. To prepare the assignment, the groups of international students (from Finland, Russia, Ukraine, Poland, Japan, Czech Republic, Vietnam, and China) were recommended to apply the GKMF to structure their group work as a suggestion. However, all four groups used the framework to organize their work and to structure their essay or final group presentation. Besides, they intuitively followed the above mentioned KM design process guide: Firstly, they identified the (different) contexts and potential barriers, before designing related business and knowledge processes. In a second step, they suggested a corresponding infrastructure as well as supporting interventions on different levels. Finally, the students had to develop an approach to evaluate their project based on what is already suggested during the first part of the case study. During the assignment, the students divided their tasks to different group members. There were experts in context/culture, instruments, processes, and performance. They used the above provided tables as a certain kind of work template – explaining that these are (first) potential, not complete categories and attributes. After that – to work on the main relations between the different framework components – they were asked to discuss their answers in their group. The four different assignments show that the GKMF was adequate to design a global KM project. The framework guided successively the work of the students dividing the big project in different work packages and milestones. However, some teams struggled with the comprehensiveness of the framework as the provided sample attributes and their corresponding references are quite demanding and could lead to a kind of information overload or disorientation. The latter is of course the contrary of what this framework was built for. For that reason, the evaluation of the framework in additional settings is of integral importance to adapt the current subset of attributes to specific contexts. Moreover, we learnt that students sometimes had quite big problems to cope with the main relations between the various dimensions of the framework. This could of course be related to the fictional environment of the case study that would need a lot more of background information or assumptions regarding Securitech Ltd. However, the complex task was quite a challenge for several students from different countries and different disciplines. Finally, it also indicates that in the near future the relation between the several parts of the framework must be elaborated in detail.

5. Conclusion and future research

In this paper, we have created a solution space for global KM by providing the Global Knowledge Management Framework identifying and harmonizing KM research efforts in the global context. Based on two scenarios and one proof-of-concept case study, we were able to observe the usefulness of this framework as an artifact and to identify further research needs. Even though our first validations have shown that the framework is applicable to knowledge management cases and scenarios in different domains for a target group with basic KM knowledge, it still needs additional validation. The scenarios as well as the case study are limited, methodologically as well as regarding the application domain. Further studies for other domains, contexts, and stakeholders need to be performed to understand the generalizability of the GKMF framework. Validating the framework, we will take into account how its theoretical foundation as well as its practical relevance and applicability in practice is (cf. Fettke and Loos, 2003a, Frank, 2007). Applying the framework will create theoretical contributions as well as practices in different domains and finally lead to its validation with regard to:



Usefulness: How does the framework support potential users?

Adaptability: How can the framework be adapted for different contexts?

Understanding: How is the framework understood by different stakeholders?

Comparative value: How does the framework improve comparability of different contexts?

Contribution to theory-building: How does the framework support theory building in the domain?

As a next step, we intend to utilize the model for identifying further research gaps and directions as well as applying and assessing the framework in different contexts. We believe that the GKMF can contribute to theory building, provide research-led guidance, create comparative research models and to serve as an evaluation opportunity for actors in the field.

References

Alavi, M., and Leidner, D.E. (2001) Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues, MISQ, Vol. 25, No 1, pp 107-136.

Argyris, C. (1990) Overcoming Organizational Defenses, Prentice Hall. Bick, M. (2004) Knowledge Management Support System, Doctoral Thesis, University of Duisburg-Essen. Bick, M. and Pawlowski J. M. (2009) Applying Context Metadata in Ambient Knowledge and Learning Environments –

A process-oriented Perspective, Hinkelmann, K., Wache, H. (Eds.): 5th Conference on Professional Knowledge Management – Experiences and Visions, pp 52-61, Bonn, Köllen Druck+Verlag GmbH.

Bick, M., Pawlowski, J. M. and Lehner, F. (2011) Global Knowledge Management – Schedule, Groups, Materials. https://docs.google.com/document/d/1A_29OywriCFnYzGLr4wUubeeVaQ50-0a-ggmqrq4xfQ/edit?pli=1

Bhagat, R.S., Kedia, B.L., Harveston, P.D., Triandis, H.C. (2002) Cultural Variations in the Cross-Border Transfer of Organizational Knowledge: An Integrative Framework, The Academy of Management Review, Vol. 27, No. 2, pp 204-221.

Bose, R. (2004) Knowledge management metrics, Industrial Management & Data Systems, Vol. 104, No.6, pp 457-468.

Coakes, E., Amar, A.D., and Luisa Granados,M.L. (2010) Knowledge management, strategy, and technology: a global snapshot", Journal of Enterprise Information Management, Vol. 23, No. 3, pp 282 – 304.

CEN (2004) CEN CWA 14924 European Guide to good Practice in Knowledge Management - Part 1 to 5, Brussels. Cummings, J.N. (2004) Work Groups, Structural Diversity, and Knowledge Sharing in a Global Organization,

Management Science, Vol. 50, No. 3, pp 352-364. Davenport, Th. H. and Prusak, L. (1998), Wenn ihr Unternehmen wüßte, was es alles weiß..: Das Praxisbuch zum

Wissensmanagement. Moderne Industrie, Landsberg/Lech 1998. De Long, D. W. and Fahey, L. (2000) Diagnosing cultural barriers to knowledge management. Academy of

Management Executive, Vol. 14, No. 4, pp 113-128. Desouza K, and Evaristo R. (2003) Global knowledge management strategies. European Management Journal, Vol.

21, No. 1, pp 62–67. Earl, M. (2001) Knowledge management strategies: toward a taxonomy’’, Journal of Management Information

Systems, Vol. 18, No. 1, pp 215-33. Eppler, M. J. (2003) The Pragmatic Development and Use of Know-how: Knowledge Management Light at Securitech

Ltd. http://www.knowledgemedia.org/files/cms/files/Securitech%20Ltd%20eng.pdf Fahey, L. and Prusak, L. (1998) The Eleven Deadliest Sins of Knowledge Management, California Management

Review, Vol. 40, No. 3, pp 265-276. Fettke, P., and Loos, P. (2003a) Multiperspective Evaluation of Reference Models – Towards a Framework, Lecture

Notes in Computer Science: Conceptual Modeling for Novel Application Domains, pp 80-91. Springer. Fettke, P. and Loos, P. (2003b) Classification of reference models—a methodology and its application, Information

Systems and e-Business Management, Vol. 1, No. 1, pp 35–53. Frank, U. (2007) Evaluation of Reference Models, In; Fettke, P. and Loos, P. (eds.) Reference Modeling for Business

Systems Approach, pp 118-140, Idea Group. Gibbs, J., Kraemer, K.L., and Dedrick, J. (2002) Environment and Policy Factors Shaping E-commerce Diffusion: A

Cross-Country Comparison. UC Irvine: Center for Research on Information Technology and Organizations. Gold, A.H.; Malhotra, A.; and Segars, A.H. (2001) Knowledge management: An organizational capabilities

perspective. Journal of Management Information Systems, Vol. 18, No. 1, pp 185-214. Grossman, M. (2006) An Overview of Knowledge Management Assessment Approaches. Journal of American

Academy of Business Cambridge, Vol. 8, No. 2, pp 242-247. Grover, V., and Davenport, T.H. (2001) General perspectives on knowledge management: Fostering a research

agenda. Journal of Management Information Systems, Vol. 18, No. 1, pp 5-21. Hansen, M. T., Nohria, N., Tierney, Th. (1999) What’s Your Strategy for Managing Knowledge? Harvard Business

Review, Vol 77, No. 3-4, pp 106–116.



Hahn, J., and Subramani, M.R. (2000) A framework of knowledge management systems: issues and challenges for theory and practice. In: Proceedings of the twenty-first international conference on information systems, Brisbane, Australia, pp 302–312.

Heisig, P. (2009) Harmonisation of knowledge management – comparing 160 KM frameworks around the globe", Journal of Knowledge Management, Vol. 13, No. 4, pp 4 – 31.

Holsapple, W., Joshi, K.D. (2000) An investigation of factors that influence the management of knowledge in organizations, The Journal of Strategic Information Systems, Vol., No. 2-3, pp 235-261

Holden, N. (2001) Knowledge management: raising the spectre of the cross-cultural dimension, Knowledge and Process Management, Vol. 8, No. 3, pp 155-163.

Holden, N. (2002) Cross-Cultural Management – A Knowledge Management Perspective. Harlow: Prentice Hall. ISO/IEC (2005) ISO/IEC 19796-1:2005. Information Technology - Learning, Education, and Training - Quality

Management, Assurance and Metrics - Part 1: General Approach. International Organization for Standardization.

Jennex, M.E. (2008) The Impact of Culture and Context on Knowledge Management, Jennex, M.E. (Ed.) Current Issues in Knowledge Management, Information Science Reference.

Jennex, M.E., and Olfman, L. (2004) Assessing Knowledge Management Success/Effectiveness Models, 37th Hawaii International Conference on System Sciences, IEEE Computer Society.

Jennex, M.E. and Olfman, L. (2005) Assessing Knowledge Management Success, International Journal of Knowledge Management, Vol. 1, No. 2, pp 33-49.

Jennex, M.E. and Olfman, L. (2006) A Model of Knowledge Management Success, International Journal of Knowledge Management, Vol. 2, No. 3, pp 51-68.

Jennex, M.E. and Smolnik, S. (2008) Towards Measuring Knowledge Management Success, International Conference on System Sciences.IEEE: Hawaii. pp 1-8.

Lee, K.C., Lee, S., Kang, I.W. (2005) KMPI: Measuring Knowledge Management Performance, Information & Management, Vol. 42, No. 3, pp 469-482.

Lindsey, K. (2002) Measuring Knowledge Management Effectiveness: A Task-Contingent Organizational Capabilities Perspective, Eighth Americas Conference on Information Systems, pp. 2085-2090.

Kalkan, V.J. (2008) An overall view of knowledge management challenges for global business, Business Process Management Journal, Vol. 14, No. 3, pp 390-400.

Kalz, M., Specht, M., Nadolski, R., Bastiaens, Y., Leirs, N., and Pawlowski, J.M. (2010) OpenScout: Competence based management education with community-improved open educational resources, 17th EDINEB Conference: Crossing borders in Education and Work-based Learning, London, UK, June 2010.

Kankanhalli, A., Tan, B.C.Y. (2005) Knowledge Management Metrics: A Review and Directions for Future Research, International Journal of Knowledge Management, Vol. 1, No. 2, pp 20-32.

Kulkarni, U.R., Ravindran, S., and Freeze, R. (2006) A knowledge management success model: Theoretical development and empirical validation. Journal of Management Information Systems, Vol. 23, No. 3, pp 309–347.

Leidner, D.E., Alavi, M., and Kayworth, T. (2006) The Role of Culture in Knowledge Management: A Case Study of Two Global Firms," International Journal of e-Collaboration, Vol. 2, No. 1, pp 17-40.

Lehner, F. and Haas, N. (2010) Knowledge Management Success Factors – Proposal of an Empirical Research, Electronic Journal of Knowledge Management Vol. 8, No. 1, pp 79– 90

Lindsey, K. (2002) Measuring Knowledge Management Effectiveness: A Task-Contingent Organizational Capabilities Perspective, AMCIS 2002, pp 2085-2090.

Lugger, K. and Kraus, H. (2001) Mastering Human barriers in Knowledge Management, Journal of Universal Computer Science Vol. 7, No. 6, pp 488-497.

Maier, R. (2007) Knowledge Management Systems: Information and Communication Technologies for Knowledge Management, 3rd ed. Springer, Berlin et al.

Maier, R., and Remus, U. (2003) Implementing process-oriented knowledge management strategies, Journal of Knowledge Management, Vol. 7, No. 4, pp 62–74.

Maier, R. and Thalmann, S. (2010) Using personas for designing knowledge and learning services: results of an ethnographically informed study, International Journal of Technology Enhanced Learning, Vol. 2, No. 1/2, pp58–74.

March, J. G. and Olsen J. P. (1976) Ambiguity and Choice in Organizations, Bergen (Norway), Universitetsforlaget. Massey, A.P., Montoya-Weiss, M.M. and O’Driscoll, T.M. (2002) Knowledge Management in Pursuit of Performance:

Insights from Nortel Networks,‖ MISQ, Vol. 26, No. 3, pp 269-289. McDermott, R. and O’Dell, C. (2001) Overcoming culture barriers to sharing knowledge, Journal of Knowledge

Management, Vol. 5, No. 1, pp 76-85. Mentzas, G. N., Apostolou, D., Young, R. et al. (2001) Knowledge Networking: A Holistic Approach, Method and Tool

for Leveraging Corporate Knowledge, Journal of Knowledge Management Vol. 5, No 1, pp 94–106. n.a. (2011) Courses of the 21th Jyväskylä Summer School

https://www.jyu.fi/science/muut_yksikot/summerschool/en/courses Nonaka, I., Takeuchi, H. (1995) The Knowlege-Creating Company. Oxford University. Press, Oxford.



Pauleen, D. (Ed.) (2006) Cross-cultural perspectives on knowledge management, Westport, Conn.: Libraries Unlimited.

Pawlowski, J.M., Holtkamp, P., and Kalb, H. (2010) Globalization Competences in Information Systems and E-Learning, Workshop on Competencies for the Globalization of Information Systems in Knowledge-Intensive Settings, ICSOB, June 2010.

Pirkkalainen, H., Thalmann, S., Pawlowski, J.M., Bick, M., Holtkamp, P., Ha, K.H. (2010) Internationalization Processes for Open Educational Resources, Workshop on Competencies for the Globalization of Information Systems in Knowledge-Intensive Settings, ICSOB, June 2010.

Polanyi, M. (1966) The Tacit Dimension. Routledge & Kegan Paul Ltd., London. Prikladnicki R, Audy J, and Evaristo R (2003) Global software development in practice lessons learned. Software

Process: Improvement and Practice, Vol. 8, No. 4, pp 267–281. Probst, G., Raub, S., and Romhardt, K. (1999) Managing Knowledge. Building Blocks for SuccessManaging

Knowledge. Building Blocks for Success. Wiley & Sons. Remus, U., Schub, S. (2003) A blueprint for the implementation of process-oriented knowledge management,

Knowledge and Process Management Journal, Vol. 10, No.4, pp 237-253. Richter, T., and Pawlowski, J.M. (2008) Adaptation of E-Learning Environments: Determining National Differences

through Context Metadata. TRANS - Internet Journal for Cultural Studies, Vol. 17. Riege, A. (2006) Three-dozen knowledge-sharing barriers managers must consider, Journal of Knowledge

Management, Vol. 18, No. 3, pp 18-35. Ryle, G. (1949) The Concept of Mind. The University of Chicago Press, Chicago. Sangwan, R., Bass, M., Mullick, N., Paulish, D.J., and Kazmeier, J. (2006) Global Software Development Handbook,

Auerback Publications. Shin, M. (2004) A framework for evaluating economics of knowledge management systems, Information &

Management, Vol. 42, pp 179–196. Stewart, Th. A. (1998) Intellectual Capital: The New Wealth of Organizations, Crown Business. Szulanski, G. (1996) Exploring Internal Stickiness: Impediments to the Transfer of Best Practice Within the Firm,

Strategic Management Journal, Vol. 17, Winter Special Issue, pp 27-43. Thalmann, S. (2011) Decision Support Framework for Selecting Techniques to Prepeare Knowledge Elements for

Adaptive Use, Doctoral Dissertation, University of Innsbruck. UN/CEFACT & OASIS (2001) ebXML Catalog of Common Business Processes v1.0,

http://www.ebxml.org/specs/bpPROC.pdf Urbach, N., Smolnik, S. and Riempp, G. (2009) Development and Validation of a Model for Assessing the Success of

Employee Portals, Proceedings of the 17th European Conference on Information Systems, Verona, Italy. Yin, R. K. (2003) Case Study Research - Design and Methods, 5th ed., Vol. 45. Thousand Oaks, California: Sage

Publications, Inc.