computer supported knowledge interaction working paper

8/9/2019 Computer Supported Knowledge Interaction WORKING PAPER

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Rohde and Sundaram, 2010 Computer-Supported Knowledge Network Interac

Working Paper, Next Generation Knowledge Networks, http://nexnet.wordpress.com/

Next Generation Knowledge Networks, Working Paper 1

Computer-Supported Knowledge Network Interaction

Max Erik Rohde and David Sundaram

Next Generation Knowledge Networks, http://nexnet.wordpress.com

WORKING PAPER, APRIL 2010

ABSTRACT (REQUIRED)

Although many explicit or implicit networks govern the reality in todays organizations, a limited selection of tools is

available to support individuals and organizations in creating, manipulating, and navigating such networks. This article

attempts a conceptual investigation of what could constitute a knowledge network in an organizational context. It is found

that knowledge should be understood as a multidimensional context-depended and dynamic construct, which can only to

some extent be brought to a formalized form. The article further investigates what kind of interactions between a knowledge

network and an actor and synthesizes these to a general framework for knowledge network interaction. This framework

differentiates the first order interactions of network manipulation into the receiving of parts of the network, and the sharing of

parts of the network; the combination of these interactions result in higher order interactions, which describe network

composition, collaboration, communication and orchestration. A brief investigation reflects on how well these modes of

interaction are supported in popular software.

Keywords (Required)

Knowledge Management, Networks, Knowledge Management Systems, Knowledge Management Methods

INTRODUCTION

Networks have been found to be a powerful conceptualization of many aspects of the physical and social world (Barabasi,

2003; Granovetter, 1973; Nagurney and Dong, 2002). In an organizational context, social networks, network organizations,

computer networks, websites and many other forms of networks play an important part in everyday life. However, there are a

limited number of tools that would allow organizations to work with such networks in a fashion that is coordinated, seamless

and integrated with natural ways of working.

This might partly stem from a lack of sound theoretical underpinnings of knowledge as a network in the organizational

context and the possible implications of such a knowledge conceptualization on organizational information systems. In thisarticle, we provide an idealized conceptualization of knowledge as a network. We further investigate a possible structured

method to work with such networks and form a number of basic network interactions. We synthesize these interactions into a

framework that can help in understanding the central requirements for any system meant to support the work with knowledge

networks.

For this, we first investigate perspectives on knowledge, which can guide the conceptualization of knowledge as a network.

We are especially concerned with whether these perspectives of knowledge would allow the encoding of knowledge in a

formalized network. Based on the discussion of knowledge perspectives, we conceptually derive basic interactions with

knowledge networks. These interactions are synthesized into a framework, which we apply in an exploration of popular

classes of software products in regards to their capability to support the interaction with a knowledge network.

KNOWLEDGE PERSPECTIVES

Doubtlessly, many different perspectives on knowledge exist in the information system and related disciplines. Our particular

focus lies on the duality between the static aspects of knowledge or knowledge as capability, the knowledge network, and theapplication and/or retrieval of knowledge, the interaction with these knowledge networks. In particular, we want to

investigate how far the distributed knowledge capability of organizations could or could not be formalized in a network

representation.

Knowledge as Multidimensional, Context-Dependent and Dynamic Capability

Organizations as a whole are said to have a set of socially constructed knowledge capabilities, which differentiate them from

their competitors (Feldman and Pentland, 2003). Different kinds of knowledge capabilities have been identified in the

organizational literature (Blackler, 1995): (1) Encoded knowledge is the kind of knowledge traditionally dealt with in the

domains of artificial intelligence and knowledge representation: this knowledge can be broken down to a distinct set of facts


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or statements, which can be represented in a symbolic form, for instance databases or knowledge bases. (2) Embrained

knowledge refers to knowledge that is applied by individuals in a conscious and analytical fashion. (3) Embodied knowledge,

in contrast, is knowledge individuals apply in an intuitive way, without being explicitly aware of what exact knowledge they

apply or posses. Many managers say they based their best decisions on intuition without being able to name what knowledge

or skills they have applied (Simon, 1987). (4) Embedded knowledge refers to knowledge implicitly existent in organizational

routines, structures, and resources, which has not been explicitly formalized. (5) Encultured knowledge is a very tacit form of

knowledge, expressed in shared beliefs or understandings of groups. This very helpful classification of the different kinds of

organizational knowledge capabilities shows that, when investigating organizational knowledge, far more needs to be

considered than what can be explicitly encoded in knowledge bases or knowledge repositories. In fact, only one aspect of

organizational knowledge, encoded knowledge, can be explicitly encoded while all the other kinds of knowledge identified

by Blackler are tacit and cannot be brought into a firm representation.

Furthermore, it is argued that knowledge must not be seen apart from the context of its application (Thompson and Walsham,

2004). Thompson and Walsham argue that each of Blacklers knowledge types, encoded, embrained, embodied, embedded,

and encultured, is to be seen as contextual component. These components are applied in a certain context, and the

experiences, which are made in due course, again become contextual components being applied in a different context. The

important implication of this perspective is that there is no such thing as static knowledge, which lies as silent capability

inside an organization; knowledge is always to be seen relative to its application in a certain context. Orlikowski (2002)

provides a related perspective of organizational knowledge, introducing knowing as dynamic, distributed organizational

capability that emerges from individual actions in certain situations. These perspectives on knowledge and its application

highlight that the formalization and representation of knowledge must never be seen apart from its application. In our terms,it is not advisable to engage with knowledge networks solely from a static perspective, but only in combination with a

dynamic perspective rooted in the application or work with this knowledge. Related notions can be found in the

organizational literature, where it is claimed that organizational routines should never be seen as static but always as adapted

to the context of their application (Feldman and Pentland, 2003). In fact, the standard way of conducting a routine, is

supposed to never be found in the real execution where the complex environment forces organisations to adapt in every

instantiation of a routine (Levinthal and Rerup, 2006).

Knowledge as a Network

Many researchers have expressed knowledge or knowledge related phenomena as networks, for instance as social networks

(Granovetter, 1973), inter-organizational knowledge networks (Dyer and Nobeoka, 2000), or networks of practice

(Agterberg, van den Hooff, Huysman, and Soekijad, 2010). It is suggested that organizations, too, can be organized as

networks (Powell, Staw, and Cummings, 1990) and, of course, the concept of human knowledge as a network (Heuer, 1999).

Furthermore, most knowledge representation (Mylopoulos, 1981), modeling (Geoffrion, 1987) and knowledge engineering

approaches (Studer, Benjamins, and Fensel, 1998) can be seen as network based.

Many of the network conceptualizations mentioned above are relevant in an organizational context when considering our

knowledge conceptualization: Human knowledge as a network becomes relevant for embrained and embodied knowledge

components, organizational structures as networks become relevant for embedded components of knowledge, encultured

components of knowledge could be reflected in social networks, and encoded components can be expressed as encoded

networks, such as a number of interlinked websites. However, as we further see knowledge as deeply embedded in a rich

context, it is very likely that a complete knowledge network of an organization is comprised of more than one of these

networks.

Instead of attempting to support each of the different networks individually, we follow an idea introduced by the emerging

discipline of network science (Barabasi, 2003): we focus on what these diverse networks might have in common. Our

discussion of the multidimensional nature of knowledge shows that knowledge is always to be seen in a very rich context,

comprised of potentially many different contextual components. This complexity seems to make integrating the different

knowledge networks a futile endeavor. However, there is one common principle to all networks: that they are constructedfrom nodes and vertices. Using Blacklers types of knowledge, we presume that all nodes and vertices belong into the domain

of each of the mentioned components of knowledge. Furthermore, we presume that an organizational knowledge network can

be disaggregated to a number of sub-networks, such as the social network underlying the organization. These few

assumptions are our understanding of what could conceptually make up a complete knowledge networkin an organizational

context.

The image of knowledge as deeply embedded in context and composed of multidimensional tacit and explicit components

seems to inherently hinder the formalization of these complete knowledge networks. As we are concerned with how work

with such networks can be supported with information technology, we need a narrower and more specific conceptualization


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of knowledge networks: formalized knowledge networks represent the fraction of the complete knowledge networks, which

can be encoded (Figure 1). A social network, for instance, can be, using social networking systems, encoded to some degree,

which will be reflected as projections of some aspects of the formalized knowledge network. However, there will always be

important aspects of the social network, which will remain tacit. Consequently, when dealing with this the formalized

knowledge network, one must always consider that one is always just working with a fraction of what makes up actionable

knowledge in an organizational context.

Figure 1 Knowledge Networks

KNOWLEDGE NETWORK INTERACTION

When knowledge can be understood and at least partly formalized and represented as a network, then there should be a way

in which actors can interact with such networks. We therefore attempt to start with a simple understanding of how an actor

would want to interact with a knowledge network from a conceptual point of view. Given the simple basic structure of

networks, these interactions are surprisingly few in number.

We first derive the interactions theoretically and in a second step reflect on these interactions briefly by investigating the

degree to which they are supported by popular software products. The conceptualization of knowledge network interaction

provided here only refers to formalized knowledge networks as we are concerned with the support of these interactions with

information technology.

Actor-Network Interaction

We are concerned with basic interactions that an actor may encounter with a network. In our view, there are two kinds of

interactions, which are fundamental for the work with any network: and actor can, firstly, change a network and, secondly,

receive or view parts of the network. In human-computer interaction, the equivalent to these operations could, for instance, be

found in the distinction between action language and presentation language (Norman and Draper, 1986). We will first explain

these two interactions and then introduce a third action, sharing parts of the network, which we perceive as being another

fundamental interaction with networks because of the omnipresence of collaborative and social systems.

Manipulation of a network can very simply be described as the operations of adding nodes and vertices as well as removingthem. This can, to some degree, be seen as the network expression of knowledge creation (Nonaka and Takeuchi, 1995) or

knowledge recombination (Kogut and Zander, 1992; Thompson and Walsham, 2004). Vertex and nodes are just conceptual

constructs in this interaction. If a real system is under investigation using this network conceptualization, the analyst must

decide which constructs represent nodes and vertices. In a webpage, for instance, pages can be nodes and hyperlinks can be

vertices. Figure 1 shows a simple network with nodes and vertices. Manipulation of the networks in real systems, can, due tothe characteristics of the system, be of different quality spanning the dimension between no manipulation possible, describing

a static network, and perfect manipulation possible, describing a totally dynamic network. The most important distinguishing

feature for the quality of manipulation is the degree of freedom an actor has in choosing where nodes and vertices should be

added or removed. In the World Wide Web (WWW), for instance, a user is usually not able to change the structure of

google.com but is possibly able to add or remove links on a his or her personal website. A second important measure for the

quality of manipulation is the heterogeneity of nodes and vertices, which can be added to the network. In a file system, for

instance, nodes could be represented as files. The variety of file types an actor can add to file system could give an indication


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of the heterogeneity of manipulation for that network. Basically, good manipulative features mean that the actor is enabled to

change the network exactly there where it deems changes necessary.

Figure 2 Two Examples of Network Manipulation

The interaction ofreceivingnodes and vertices from a network is a further fundamental interaction. When a user for instance

opens a webpage in a browser, he or she would receive the node of the page browsed to, as well as the vertices describing the

outgoing links of that webpage. Receiving can be triggered either in a push or pull fashion (Mercurio and Erickson,

1990). In the case of pull, the actor must actively seek which nodes and vertices he or she wants to receive. The given

example of browsing to a webpage describes such a situation. In the case of push, an intelligent system decides which

nodes and vertices are sent to the user; a notification of a new email in the inbox would fit into this schema. The quality of

push and pull content delivered to the actor can be described in terms of the relevance of the nodes and vertices for the actor.

In case of pull, if the actor has to receive many nodes, in a way jumping from one node to the other, until it is able to find thenode it is searching for, the receiving interaction is of not so good quality. One important aspect governing this in a network

is, for instance, the average length of a path connecting any two nodes (Barabasi, 2003). Figure 3 illustrates a pull way of

interaction with a network, navigating it from node to node, and a push way, where a certain node is pushed to the actor.

Figure 3 Navigate and Notify Interactions

The interactions of manipulation and receiving are closely related. The actor would not be able to change the network where

it deems it necessary if it would not see the network. When an actor can interact with a network with both a high quality of

manipulation and receiving, composition of this network might be enabled. Composition is a second-order interaction with a

network which depends on the two first order interactions of manipulate and receive. This sentence is confusing and uses two

different tenses. Maybe something like: Composition, in simple terms, refers to creating new sub-networks by combining or

recombining existing nodes of the network instead of creating something from scratch. Therefore, In order to compose a

network, there be means to navigate or be notified of nodes that can be combined and the capability to feed new nodes and

vertices to the network, as well as recombining them. Composition stems from the context of service-oriented architecture

(e.g. Bhattacharya, Caswell, Kumaran, Nigam, and Wu, 2007; Emig et al., 2006) where atomic services are composed to

more complex services in order to meet new requirements. Furthermore, composition is used in the context of modeling,

where new models are composed out of already existing models (Krishnan and Chari, 2000).

Sharingis a mode of interaction, where a part of the network is sent from one actor to the other. This could involve differentnotions of knowledge sharing in different levels of aggregation such as with yourself over time (Kim, 1993; Lansdale, 1988),

with other individuals in direct interaction (Orlikowski, 2002; Thompson and Walsham, 2004), within a team (Eppler and

Sukowski, 2000; Walz, Elam, and Curtis, 1993), between groups/teams in an organization (Carlile, 2002; Tanriverdi, 2005)

and between organizations in a business network (Dyer and Nobeoka, 2000). One measure for the ability of interaction that a

knowledge network offers in regard to sharing is, therefore, who it allows collaborating with. To take this notion further, it

should also be possible for actors to restrict access to nodes of the networks to the exact collaborator with whom the actor

wants to share the notes. A second measure is the kind of collaboration it offers following the time space taxonomy (Ellis,

Gibbs, and Rein, 1991; Johansen, 1988).


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If both sharing and manipulation are enabled, the network can then be worked with in a collaborative effort and we can speak

of network collaboration. If sharing and receiving are enabled, network communication in passive terms of sending parts of

the network without changing them is enabled. As the interactions of network collaboration and communication both depend

on first order interaction, they are classified as second order interactions with a network.

If all the first order interactions manipulate, receive and share are enabled, network orchestration becomes possible.

Orchestration can be described as collaborative composition; meaning more than one actor composes existing nodes into new

nodes in a collaborative effort. Similar to the term composition, orchestration is used in the context of service orientedarchitecture to describe how services can be organized in a decentralized and collaborative fashion (Emig et al., 2006).

Orchestration as the collaborative composition of heterogeneous information doubtlessly comes close to how organizational

knowledge work can be understood as a collaborative, social effort (Orlikowski, 2002). In model management, orchestration

could be understood as a truly collaborative effort to abstract or simplify complex and interrelated models; a task not often

fulfilled by model management systems (Liew and Sundaram, 2009).

We provide a synthesized view in the form of a framework based on the interactions and their interrelations discussed above.

The framework is given in Figure 4. This framework does not aspire to describe all issues which are related to the work with

networks; it has the particular focus on describing possible modes of interactions with a network from an actor perspective;

issues such as the storage, management or governance of networks are intentionally not considered by this framework.

Figure 4 Knowledge Network Interaction Framework

NETWORK INTERACTION IN POPULAR SOFTWARE

In order to analyze whether current software products support network interaction as described in our proposed framework,

we analyze the software products that we reasonably expect people to use frequently. We see the advantage of this as

twofold: firstly, we can investigate whether our modes of interaction can be recognized in popular software products, and,

secondly, findings of lack of support for modes of interaction in popular software products can guide in the design and

implementation of solutions, which enable complex network interaction.

As we have discussed earlier, the knowledge network and the interaction with the knowledge network shall not be seen apart

from each other. When looking at popular software products, we must therefore consider the basic data structure underlying

these applications as well as the interface, which these products offer. We chose as units of analysis the following classes ofsoftware products: file browser, communication software and web browser; each of these have a basic data structure, which

are files/folders, messages/threads, and the World Wide Web (WWW) respectively.

For each of the classes, we have identified how the modes of network interaction are represented in these products and

assessed how well the modes are supported by the products. As the modes of interaction are specified in rather broad terms,

we follow a qualitative approach to data analysis. We have created records of what comprises the network analogy in the

individual products and where they are represented in the interfaces. Starting from there, we have assessed whether the

qualitative measures for the individual modes of interaction are met in the different products. Given the limited space in this


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paper, we provide a synthesized description for the classes of software products, listing their strongest and weakest points in

regards to network interaction found in the product classes.

The manipulation of networks is a big strength of file systems. Files and folders can flexibly be created or moved. Navigation

and collaboration, on the other hand, are not very well supported in the interaction with file systems. Often the absence of

shortcuts leads to long paths through the network, inhibiting efficient navigation. Collaboration, though it is not directly

supported on local file systems, can be accomplished using mapped network drives. However, many aspects of file systems

do not work very well in combination with mapped network drives, for instance, shortcuts or links.

The major strength of communication software is collaboration. They are usually powerful tools to share nodes with one or

more other users. Though social networks often support a greater variety of collaborative interactions, the message clients

under investigation are still the most widely used communication tools. Depending on the use of the clients, they allow the

different place modalities of the time space matrix. However, communication software are weak in supporting recombination

and navigation. Though communication software can process heterogeneous information, the precision in which nodes and

vertices can be manipulated is low.

A major strength of the web browsers lies in the efficient navigation of the World Wide Web. It is easy for users to navigate

from one page to the other. The difficulty of interaction with web browsers lies in the manipulation of the network as argued

above. The architecture of the web further determines that, though it provides efficient means to publish information, it

provides fewer means for different modes of sharing such as in 1-to-1 communication.

In all the products under investigation, we were able to find an analogy for knowledge network and the interaction with such

networks. This indicates that the conceptualization of work with information as knowledge network interaction can beapplied to rather different software products and provides a normalized way to assess them. However, this conceptualization

is just one way of conceptualizing work with knowledge and our study aims to illustrate this perspective, not to claim the

superiority of this conceptualization to other conceptualizations.

We have tried to choose three classes of software products and associated data structures, which exhibit very different ways

of interacting with information. Our choice is not meant to be representative for all software products but rather is guided by

the popularity of these forms of interacting with information. Our analysis has shown that each of these ways of interacting

with the knowledge network has its unique strengths and weaknesses. Figure 5 shows each of the classes placed in our

framework according to their strength and weaknesses in regards to the individual interactions.

Figure 5 Popular classes of software products mapped in the knowledge network interaction framework

The higher lever interaction network composition and orchestration both require a product to support more than one

dimension of network interaction. Figure 5 illustrates that, as the software products under investigation primarily meet the

requirements of one dimension, they fail to fill the space of network composition, communication, collaboration and

orchestration.

That the software products we have investigated do not support these higher-level modes of interaction does not necessarily

mean that there would be no software available that could support them. In fact, many semantic web based implementations


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have a stronger support for such interactions (Auer, Dietzold, and Riechert, 2006; Beydoun, Kultchitsky, and Manasseh,

2007; Sintek, van Elst, Scerri, and Handschuh, 2007). However, that we, in our every day work, often do not interact with

such advanced tools indicates that there is still room for improvement in terms of providing tools that allow all modes of

knowledge network interaction.

CONCLUSION

There has been significant research on the representation and formalization of knowledge in the domain of mathematics,

modelling and computer science. Especially the semantic web is hereby of great interest; a technology which is founded on a

network understanding of knowledge. In social research, there is a upraising focus on the importance of social networks,

being fuelled by the recent advent of social networking websites; making this a particularly researched area in the discipline

of information systems. Although there are some attempts to link new knowledge formalizations with social networks, there

is little research on how the social and formal sub-networks of knowledge could be integrated in one conceptualization.

Our article provides a first exploration of how knowledge in an organizational context could be conceptualized as a network,

and, more importantly, how actors can interact with such networks. Such a conceptualization could ultimately lead to a

design foundation (Markus, Majchrzak, and Gasser, 2002; Walls, Widmeyer, and El-Sawy, 1992), which can help in the

design, implementation, and evaluation of next generation knowledge management systems.

We believe that understanding and conceptualizing the complex knowledge in an organizational context can possibly help to

focus investigations in this tacit domain. To allow this, future research could greatly substantiate the conceptual ideas

presented in this paper with further empirical or design science oriented studies.

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