Supporting Knowledge and Expertise Finding within Australia's Defence Science and Technology Organisation
Supporting Knowledge and Expertise Finding within Australia's Defence Science 
 and Technology Organisation 
Paul Prekop 
DSTO Fern Hill, Department of Defence, Canberra ACT 2600 
paul.prekop@dsto.defence.gov.au 
Abstract 
This paper reports on work aimed at supporting 
knowledge and expertise finding within a large 
Research and Development (R&D) organisation. The 
paper first discusses the nature of knowledge 
important to R&D organisations and presents a 
prototype information system developed to support 
knowledge and expertise finding. The paper then 
discusses a trial of the system within an R&D 
organisation, the implications and limitations of the 
trial, and discusses future research questions.
1. Introduction 
This paper describes work undertaken to support 
knowledge and expertise finding within Australia's 
Defence Science and Technology Organisation 
(DSTO). DSTO is a government funded research and 
development (R&D) organisation, with a very broad, 
applied R&D program focused primarily within the 
defence and national security domains. DSTO employs 
approximately 1900 engineers and scientists across a 
wide range of academic disciplines (about 30% of staff 
hold PhDs), within seven sites throughout Australia. 
Like most other large R&D organisations [1, 2] and 
professional services firms, DSTO is a project-centric 
organisation; projects are formed to address specific 
questions or problems, or to develop specific products. 
The nature of the outcomes of the projects undertaken 
by DSTO varies considerably, and can range from 
academic papers and technical reports, through to 
prototype and working system development, and to 
professional services and consulting engagements.  
The work described in this paper is part of an 
ongoing knowledge management improvement 
program aimed at exploring:  
Methods to allow staff to build and maintain 
wide and detailed awareness of DSTO's past, 
current and planned projects; 
Methods to enable staff to locate other staff 
with relevant skills, interests, abilities or 
experience; 
Low cost (in terms of time and effort) methods 
to support the development of communities of 
interest, and less-formal collaboration and  
sharing within the organisation;  
Organisational cultural and behavioural issues 
that may act as barriers to effective knowledge 
and expertise sharing.  
A prototype information system, the Automated 
Research Management System (ARMS), was 
developed to explore approaches to addressing these 
issues.  
Section 2 discusses the nature of knowledge and 
knowledge management within the R&D environment, 
and describes the types of support for knowledge and 
expertise-finding needed within organisations such as 
DSTO. Section 3 describes ARMS and how it supports 
knowledge and expertise finding within DSTO. 
Section 4 outlines the ARMS trial and trial 
methodology, and Section 5 discusses the results of 
two studies undertaken as part of the ARMS trial. 
Finally, Section 6 discusses the implications and 
limitations of the work undertaken so far and describes 
potential areas for future work. 
2. The Nature of Knowledge and Expertise 
within R&D Organisations  
2.1. Theoretical Background  
The main theoretical idea underpinning this work is 
that the knowledge important to an organisation, or 
that makes it unique or gives it a competitive 
advantage, is embedded in key elements that make up 
the organisation [3–5]. 
 According to [3], this knowledge is embedded in 
three key organisational elements – the members of the 
organisation, the tools used within the organisation, 
and the tasks performed by the organisation. For many 
©1530-1605/07 $20.00     Commonwealth of Australia 2007
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
organisations, organisationally important knowledge is 
embedded within skills, experiences, expertise and 
competencies of the individuals that make up the 
organisation [1, 3, 6]. This is particularly true for R&D 
organisations [1] and other professional services 
organisations [5]. As well as people, significant 
organisational knowledge is embedded within the tools 
the organisation uses, including specialised physical 
hardware used as part of a manufacturing process, for 
example, through to conceptual or intellectual tools 
such as consulting or analysis frameworks [3, 7]. The 
third key element identified by [3] is the tasks 
performed by the organisation. Tasks reflect an 
organisation's goals, intention and purpose [3, 5, 7].  
For R&D, engineering and other professional 
services organisations, key knowledge is also 
embedded within the products or other kinds of 
outcome the organisation produces. The development 
of products or other kinds of outcome uniquely 
combines together the organisation's staff, tools and 
tasks to address a particular question or problem, or to 
develop some kind of product, and can be seen as 
uniquely embedding the application of the 
organisation's collective knowledge, skills, experiences 
and expertise within a particular domain, to address a 
particular question or problem or to develop some kind 
of product [1, 7,  9, 10]. 
As discussed in [11], knowledge management is 
centred on two, potentially limiting, philosophical 
foundations. The first is the idea that tacit and explicit 
knowledge are two distinctly different forms or types 
of knowledge, rather than simply being a dimension 
along which all knowledge exists. The underlying 
assumption that tacit and explicit knowledge are 
different leads to the conclusion that a key goal of 
knowledge management is the codification of tacit 
knowledge into explicit knowledge [12]. However, as 
[11] points out, not all knowledge can (or should) be 
codified, and any knowledge management approaches 
that rely on the codification of knowledge are likely to 
fail. The second philosophical foundation that 
knowledge management rests on is the data – 
information – knowledge continuum: the idea that 
information is in some way better data and that 
knowledge is in some way better information. As 
discussed in [11], this leads to knowledge management 
approaches that focus only on capturing and managing 
some form of codified knowledge [13], while ignoring 
data and information that could provide equal or even 
greater value to users.  
However, the view that knowledge important to an 
organisation is embedded in the key elements that 
make up the organisation potentially provides an 
approach to knowledge management that doesn't rest 
on these two potentially limiting foundations. The 
focus of a knowledge management approach that 
accepts the embeddedness of knowledge as important 
becomes one of finding and devising methods, systems 
and approaches that in some way index and expose the 
core entities within the organisation that hold the 
embedded knowledge, rather than focusing on 
codification and managing the codified knowledge. 
The importance of the knowledge embedded in the 
different organisational entities will vary with the 
nature of the organisation and the nature of the work 
performed by the organisation. The following section 
discusses the kinds of knowledge important within an 
industrial R&D organisation, and the kinds of entities 
the knowledge is likely to be embedded within.  
2.2. Knowledge and Expertise within R&D 
Organisations 
For industrial R&D organisations (and many other 
knowledge intensive firms [14]), the key knowledge 
that makes the organisation unique is embedded in the 
experience, expertise and competencies of the 
engineers and scientists that make up the organisation 
and the organisation's collective project history – the 
past products the organisation has developed, or the 
past problems or questions it has addressed [1, 10]. 
 Within most industrial R&D organisations, the 
products developed or projects undertaken require the 
application of collective individual knowledge, skills, 
experience and expertise in unique ways. As a result, 
products and projects can be seen as an embedding the 
application of the organisation's collective knowledge, 
skills, experience and expertise, within a particular 
domain, to address a particular question or problem [1, 
The information and knowledge created as part of 
past projects can provide important insights into 
finding solutions to current problems, or gaining an 
understanding of how similar problems have been 
solved in the past. Project histories can also support 
problem reformulation, and can offer some help in 
validating proposed solutions [15]. Past projects are 
also important because they can provide a link back to 
the staff who contributed. This in turn can provide 
valuable insight into the knowledge, experience and 
expertise that individual staff may have [1, 8, 15]. 
Colleagues act not only as important sources of 
information, and pointers to other sources of 
information [16], but most importantly they provide an 
interactive think along function [17, 18]. 
As discussed previously, the goal of this work was 
to develop ways of improving knowledge and 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
expertise finding. The approach taken by the work 
described in the following section was to develop an 
information system (ARMS) to act as a rich, 
interactive model of the embedded knowledge within 
the organisation – in particular, the current and past 
projects within the organisation, and the expertise, 
skills and experience of the staff that make up the 
organisation.  
3. The ARMS Prototype 
The Automated Research Management System 
(ARMS) is a prototype, web based, information system 
developed to explore approaches to supporting 
knowledge and expertise finding within DSTO.  
ARMS holds information about the key R&D 
entities relevant to DSTO – staff and projects, as well 
as formal and informal project outputs (academic 
papers, technical reports, design documents, data 
collections, and so on). Within ARMS, these entities 
are organised around the organisation's hierarchical 
structure, and around Themes – collections of 
taxonomic descriptors used to describe the client and 
scientific domains that DSTO works within. The key 
R&D entities and their relationships are shown in 
Figure 1.  
StaffStaff
ThemeTheme
ProjectProject
OutputOutput
Author Of
Created ForDescribed By
Manager Of
Contributes To
UnitUnitSiteSite
Described By
Interested In
Located At
Exists In
Exists In
Role In
Head Of
Role InMember Of
Figure 1. Conceptual domain model 
3.1. The Users' Perspective  
As discussed in Section 2, the key knowledge, 
experience, expertise and competency of an industrial 
R&D organisation exists in its project history and the 
collective expertise, skills, experience and knowledge 
of its scientists and engineers. ARMS supports 
knowledge and expertise finding by exposing both 
projects (and the outputs associated with projects) and 
staff as richly interlinked first class objects (unlike 
many similar systems [19], where staff are either not 
included, or simply included as author labels 
associated with the entities held by the system).  
From a user’s perspective, ARMS exists as a 
collection of dynamically generated web pages, with 
each R&D entity having its own web page that pulls 
together all the information related to that entity.  
Staff pages contain basic staff information, 
including: contact information, site location and 
organisational affiliation, and descriptions and links to 
the projects and project outputs the staff member has 
contributed to. This information is drawn from existing 
organisational information systems. Staff pages can 
also contain optional information, including staff 
descriptions of current and past project work, staff 
photo and basic biographical information, and current 
interests and work.  
Project pages contain project descriptions (abstract, 
overview, background, themes, etc), project 
milestones, planned deliverables, information about the 
project's relationship to other past current and planned 
projects, and the project's status. Project pages also 
contain information and links to the staff that have 
contributed to the project, as well as information about 
and links to the project's outputs. All this information 
is obtained from existing information systems. End 
users are also able to add richer project description 
information, as well as any kinds of additional outputs 
to the project's home page.  
Output pages contain basic metadata describing the 
output (title, abstract, publication details, document 
type and so on) and the documents that make up the 
output (for example MS-Word files, data files, image 
files, etc). Output pages contain information and links 
to the staff who contributed to the output, as well as 
information and links to the project the output was 
developed for. Output information contained within 
ARMS is extracted from an existing publication 
management system. End users are also able to add 
additional project outputs to any project page.  
ARMS provides multiple entry points into the data. 
Users can access staff, project and output entities 
directly via the staff, project and output browsing 
pages. Each of these pages provides filterable lists of 
the staff, project and output entities contained within 
ARMS.  
In addition to the browsing pages, users can also 
enter ARMS via a representation of the organisation's 
structure (the Unit entities shown in Figure 1), or via 
descriptions of the organisation's R&D program (the 
Themes entities shown in Figure 1). 
Each DSTO unit has a corresponding unit page that 
holds basic contact information for the unit, the unit’s 
head, administrative contacts for the unit, and the 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
unit’s structure (for example the groups within a 
branch), the staff who are members of that unit, and 
the tasks associated with that unit. This information is 
extracted from several different existing information 
systems. In addition, end users can optionally insert 
additional information describing units. Units can be 
directly entered via the unit browse page, an 
interactive form of the organisation’s hierarchical 
structure.  
DSTO's R&D program is represented as a 
collection of Themes, a combination of taxonomy 
descriptors (based around DEFTEST [20]) that cover 
the core science and technology (S&T) areas of DSTO, 
and taxonomy descriptors that cover the key client 
areas DSTO works within. S&T and client Themes are 
used to describe the staff, project and output 
information held within ARMS. Each Theme has an 
automatically generated home page that aggregates all 
the projects, staff and outputs described by the Theme. 
In many ways the Theme home pages can be seen as 
aggregating everything the organisation 'knows' about 
a particular Theme area – that is all the staff, projects 
and outputs related to that Theme. Themes can be 
directly entered via the Theme browse page, an 
interactive and structured collection of the Themes 
held by ARMS.  
An important part of ARMS is rich hyperlinking 
between the various entities that contextualises the 
information held by ARMS. Staff, for example, are 
contextualised by the projects they contribute (or have 
contributed) to and the outputs they have contributed 
to. Projects are contextualised by the staff that 
contribute to them, and the part of the organisation 
they were performed by. The intent of the 
contextualisation is to allow users to infer richer 
meanings based on explicit relationships present in the 
data. The utility of this approach, especially in terms of 
expertise finding, is discussed in Section 5.2.  
In addition to the browsing functions, ARMS also 
includes a search function that support free text 
searching over all the information held by ARMS 
(fields associated with each entity as well as full 
document text), field searching (for example, searching 
explicitly by unit name, or project number), as well as 
Theme searching. The different search types can be 
combined with Boolean operators (AND and OR) to 
form complex queries.  
3.2. Technical Perspective 
From a technical perspective, ARMS consists of a 
centralised repository that holds information extracted 
from existing corporate information systems, as well as 
a small amount of information specifically created to 
support ARMS. Access to the information and 
functions provided by ARMS is via a web based 
interface that supports the functions described in 
Section 3.1, as well as a Web Services1 interface that 
provides dynamic programmatic access to the core 
functions and information (see Figure 2). The Web 
Services interface was provided to support dynamic 
access to by ARMS by specialised applications, such 
as collaborative Microsoft SharePoint portals [22] and 
other specialised web sites. 
Taxonomy
Repository
Domain Logic 
Unstructured 
Repository 
Sem i-structured 
Repository 
Search Engine
W eb Interface W eb Services Interface
Staff 
System
Project 
Management
System
Publication 
Repository
W rapperW rapper W rapper
Collaborative 
SharePoint 
Collaborative 
SharePoint 
Collaborative 
SharePoint 
Portal
Specialised 
W eb Sites 
S&T Staff M anager
Figure 2. ARMS logical architecture  
Almost all of the information used by ARMS was 
drawn from existing corporate information systems as 
well as less-formal corporate information collections 
such as spreadsheets and intranet sites. The re-used 
corporate information generally needed to be cleaned 
and reformatted before it could be used. Most of the 
data cleaning issues encountered were common to 
other data warehousing projects [23], and included a 
lack of common record identifiers across the different 
systems, conflicting data values across the different 
systems, missing and erroneous data, and name and 
type conflicts. The data cleaning and re-formatting 
functions were encapsulated in series of customer 
wrappers that were developed for each of the core 
corporate applications and other corporate information 
sources.  
As discussed in Section 3.1, ARMS provided a 
search function over all the information held. The 
search engine combined free text searching over the 
unstructured information held by ARMS (generally 
reports, papers, presentation and other project 
outcomes) with searching over the structured and 
                                                          
1 An overview of Web Services can be found in [21]. 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
semi-structured information held. Free text search was 
provided by a commercially available text search 
engine; searching over structured and semi-structured 
information was provided by a custom developed 
search engine.  
4. The ARMS Trial  
To validate the concepts underpinning ARMS and 
its utility in supporting expertise and knowledge 
finding within DSTO, a prototype version of the 
system was evaluated within two DSTO divisions from 
June until November 2004.  
 The ARMS prototype (as described in [24]) was 
fully developed and fully populated with relevant staff, 
project and output information. The data held by 
ARMS was kept up-to-date throughout the trial period. 
During the trial period ARMS was made available to 
staff within the two divisions selected (Division A and 
Division B). These divisions were selected because 
together they reflect a good cross section of the staff, 
organisational structures, and research programs within 
DSTO. 
Division A was split across five sites, and generally 
had a multi-disciplinary, professional services focus. 
At the time of the trial it contained approximately 80 
staff. 
Division B was split across three sites, and 
generally had a computer science/software engineering 
base, with a strong R&D focus. At the time of the trial, 
it contained approximately 110 staff.  
Table 1. ARMS Trial Studies 
Study Date Collection Methods 
Study One: 
Organisational Wide 
Focus Groups 
May  Focus Groups  
Study Two: Stake-
holder Interviews 
June/July  Unstructured and 
semi-structured 
interviews 
Study Three: Concept 
and Implementation 
Survey 
July  Survey questions  
Study Four: Project 
Seeking Experiment  
September  Data seeking 
experiment
Study Five: Utility of 
Usage Study 
November  Semi-structured 
interviews and 
survey questions 
Over the trial period, five different studies were 
undertaken (see Table 1). Each of the studies aimed to 
explore the utility of ARMS from various perspectives. 
This paper discusses two of the key studies, Study 
Three and Study Five, both of which measured the 
utility of ARMS from the perspective of R&D staff. 
The Concept and Implementation Survey (Study 
Three) was undertaken after ARMS had been available 
in the two trial divisions for approximately 1½ months. 
The survey was sent to all staff in the two trial 
divisions who had used ARMS at least once during the 
trial period. Of the 75 divisional staff who had used 
ARMS at least once, 23 staff responded.  
Respondents were spread across three sites; 40% 
from Site A, 52% from Site B, and 8% from Site C. 
The respondents represented a good cross section of 
the organisation’s management structure, with 17% of 
respondents holding organisational unit management 
positions (group or branch), and 40% of respondents 
having project management responsibility. Overall the 
sample reflected a good cross-section of the 
organisation, with slight over-sampling of respondents 
from Site B. The results of this study are discussed in 
Section 5.  
The Utility of Usage Study (Study Five) was 
undertaken towards the end of the ARMS trial. The 
most frequent users of ARMS were identified, and 
invited to participate in the study. Of the users 
selected, 10 agreed to participate. Study respondents 
were spread across three sites; 70% from Site A, 20% 
from Site B and 10% from Site C. Around half of the 
respondents held project management responsibility. 
Overall, the sample in this study reflected a good cross 
section of the organisation, except for the lack of 
respondents holding organisational unit management 
positions, and an over-sampling of respondents from 
Site A.  
Study Five was run as a set of survey questions and 
semi-structured interviews. Participants were asked to 
list the functions they used ARMS to perform over the 
previous month. The interviews discussed how 
participants used ARMS, its utility to them, and how 
ARMS compared to alternative approaches they may 
have tried. The interviews were recorded, and the 
recordings transcribed. The interviews ran for an 
average of 50 minutes each.  
5. Results and Discussion  
5.1. ARMS Usage  
Studies Three and Five recorded how ARMS was 
used by participants over the previous month. Table 2, 
below, lists the functions ARMS was used to perform 
and the percentage of users who used ARMS to 
perform the listed function.  
One of the most striking features of Table 2 is the 
dramatic changes in some of the ways ARMS was used 
over the trial period. (Study Three was undertaken 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
after ARMS had been available for 1½ months; Study 
Five was undertaken toward the end of the 6 month 
trial).
Table 2. Overall ARMS Usage 
Use of ARMS  
Study
Three 
Study
Five 
1. Exploring the system; for example 
seeing what information it holds and 
exploring how I could use it. 
96% 20% 
2. Building an awareness of DSTO's 
projects and staff; for example seeing 
what work a group is doing, or what 
work is going on within a Work Area, 
or seeing what a particular person has 
been working on recently. 
61% 10% 
3. Finding out about a particular project; 
for example who is working on it, 
who the project manager is, or finding 
the various papers, reports and other 
outputs associated with the project.  
56% 40% 
4. Finding out about a person; for 
example their contact information, 
what they are working on, what 
papers and reports they have written 
or what outputs they have been 
involved in, or finding out about their 
interests or experience. 
52% 50% 
5. Finding out about a particular 
research area; for example who is 
interested in the area, which tasks 
contribute to the research area, or 
what work has been produced in the 
research area.  
35% 20% 
6. Finding out about a particular 
organisational unit; for example who 
is in the unit, what tasks the unit is 
responsible for, or finding contact 
information for the unit.  
30% 0% 
7. Finding out about a particular Client 
Area; for example who is working in, 
which tasks contribute to the Client 
Area, or what work has been 
produced for the Client Area.  
22% 0% 
8. Finding a particular formal or 
informal output.
13% 30% 
9. Searching for a person with particular 
skills, interests, experience or 
abilities. 
13% 10% 
10. Other 4% 0% 
The largest change was the drop in exploratory use 
of ARMS between the two studies. This change is 
likely to be a result of users building an understanding 
of the functions and features of ARMS as the study 
progressed. Once an understanding of ARMS (based 
on exploring the system) was developed they either 
moved to non-use, if they felt ARMS provided no 
value, or they moved to using specific features and 
functions of ARMS.  
Using ARMS to build and maintain an awareness of 
the work being performed within DSTO (Question 2), 
and using ARMS to find out about particular 
organisational units (Question 6) and client areas 
(Question 7) also dropped over the study period. The 
drop in using ARMS to perform these functions is 
likely to be due to two key factors. The semi-structured 
interviews undertaken as part of Study Five revealed 
that awareness (Question 2) is something that is built 
for a specific purpose, for example moving to a new 
organisational unit or moving into a new research area, 
and then is maintained by actively being involved in 
the organisational unit or research area. Once an 
overall awareness has been built, it is maintained by 
more direct information seeking activities, for example 
finding out about a staff member, or a project, or 
hunting up specific project outputs. 
The changes in the results for Questions 6 and 7 are 
due to similar reasons, with participants initially using 
ARMS to build a general awareness of organisational 
units and client areas, and then maintaining that 
awareness by more direct information seeking 
methods.  
The second factor likely to affect the use of ARMS 
to build and maintain awareness is a fundamental limit 
inherent within the ARMS trial. As discussed 
previously, ARMS was fully populated and maintained 
with data drawn from only two trial divisions, not the 
whole organisation. As a result the value of ARMS in 
providing a rich awareness to participants was limited 
to only the two trial divisions. This limitation and its 
likely impact on the overall trial result is discussed in 
more detail in Section 6.1.  
5.2. Finding People, Projects and Outputs 
One set of functions that ARMS was regularly used 
to perform over the study period was finding people, 
projects and outputs (Questions 3, 4 and 8 
respectively).  
As shown by Table 2, finding people was 
constantly the most frequently used function of 
ARMS. The semi-structured interviews undertaken as 
part of Study Five revealed that participants who used 
ARMS to find people were generally looking for 
colleagues who have similar research interests or 
worked in similar areas. The semi-structured 
interviews revealed that users who employed ARMS in 
this way, were, in general, seeking out connections 
with other colleagues in order to share ideas, discuss 
problems/issues, have work reviewed, gain insights and 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
different perspectives on a common/shared problems, 
and perhaps even re-use and build on the work already 
completed by colleagues.  
These findings confirm much of the previous 
research (described in Section 2) describing the 
information seeking behaviours and needs of scientists 
and engineers.  
Overall, participants reported that the information 
available via ARMS in most cases allowed them to 
identify colleagues they felt would be worth 
contacting. However, there were some limitations in 
the data ARMS provided. One key limitation was the 
scope of the information available via ARMS. As 
discussed previously, ARMS only contained 
information for the two trial divisions. This limited the 
number of colleagues users could learn about. The 
second key limitation with the staff data available via 
ARMS was that individual staff expertise, experience 
and research interests, in general, could only be 
derived by understanding the context surrounding the 
staff member (this is discussed in Section 5.3). While 
in most cases the surrounding context provided users 
with sufficient information to infer a staff member's 
expertise, experience and research interests, it did 
mean that users were generally unable to directly 
search for staff with particular expertise, experience 
and research interests but instead had to search for staff 
via outputs or projects, and infer staff relevance based 
on the relationships between staff and the projects and 
outputs they contributed to.  
An interesting question explored as part of the semi-
structured interviews was the relationship between the 
kinds of information available via ARMS, and the 
kinds of information available via the user's social 
networks. Most respondents acknowledged the 
importance their social network plays in being able to 
find colleagues with specific expertise, experience and 
research interests. However, they generally found that 
there were limits in the coverage of their social 
network; in particular many respondents felt their 
social network often didn't extend into other 
organisational sites, or into other organisational units. 
In comparison, they felt that the information held by 
ARMS was more complete and offered greater 
coverage of all parts of the organisation, and they 
viewed ARMS as a way of filling gaps in their social 
network.  
The second key group of functions regularly used 
throughout the trial was finding projects and outputs. 
The semi-structured interviews revealed that when 
searching for projects and outputs, participants were 
generally looking either to find relevant and useful 
colleagues, or were explicitly looking for information 
related to past projects. As discussed in Section 2, for 
engineers in particular, descriptions of past projects 
and the formal and informal products of projects 
(reports, papers, designs, data sets, meeting minutes, 
and so on) are important because they offer insights 
and approaches to solving past problems that may be 
useful for solving current problems. The semi-
structured interviews revealed that participants were 
using ARMS in this way.  
As well as ARMS, participants could obtain project 
and formal publication information from two existing 
organisational information systems, a Project 
Management System (PMS) and a Publications 
Repository (PR). The basic project and publication 
information held by ARMS was obtained from these 
two systems, and in most cases ARMS held little 
additional information. The key value added by ARMS 
was the rich contextualisation of the information, 
together with existing staff information to provide 
multiple entry point into the information sought, and to 
allow rich relationships and deeper meaning of the 
information to be inferred. This is discussed in more 
detail in the following section.  
5.3. Contextualisation and Navigation  
As shown by Figure 1, the information contained 
within ARMS is richly interlinked. The rich 
interlinking helps to contextualise this information, 
making it easier for ARMS users to develop a deeper 
understanding of the information held within ARMS, 
as well as providing users with multiple entry points 
into the information, and a navigation model drawn 
from the users' domains.  
The semi-structured interviews undertaken as part 
of Study 5 showed that by knowing, for example, the 
background of key staff involved in a project, users are 
able to infer more about the likely directions, 
perspectives or methodologies a project may use. Or 
by knowing about the project that created a particular 
output, users are able to build a richer understanding of 
the output's meaning.  
The rich interlinking within ARMS also provided 
users with multiple entry points into the information 
held by ARMS, and provided them with natural 
navigation paths drawn from their domain. The semi-
structured interviews revealed that almost all users 
reported that the richly contextualised nature of ARMS 
helped them navigate ARMS, allowing them to enter 
ARMS from many different directions, often using 
only incomplete information as a starting point.  
For example, several users reported browsing 
ARMS by starting with a vague awareness that a staff 
member may be doing work that might be interesting 
or relevant to them. By using ARMS, they were able to 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
move from the individual's staff page to their project 
pages – to find out about the work being performed. 
From project pages, they would move to output pages, 
to gain a deeper insight of the work being performed, 
or they would move to Theme pages, to find related 
work, staff or outputs. Other users reported a similar 
approach using projects as a starting point to find 
people or to find other related projects.  
This form of navigation also allows for far richer 
forms of accidental information discovery [25], and 
provides users with the ability to develop rich mental 
models of the organisation's past, planned and current 
work program.   
5.4. Cultural Impact  
As discussed in Section 1, one of the goals of the 
work described in this paper was to address potential 
barriers to information sharing within DSTO, 
including behavioural and cultural issues, as well as a 
lack of low cost methods, systems or processes to 
support information sharing within DSTO. Both Study 
Three and Study Five attempted to measure the likely 
influence ARMS would have on these barriers. The 
results are shown in Table 3; questions were measured 
on a 7 point, end anchored scale, with 1 meaning 
strongly disagree, and 7 meaning strongly agree.
Table 3. Cultural Impact 
Question Study Three Study Five 
If ARMS held information about everyone in the DSTO and 
all DSTO's past, current and planned work: 
1. It would be easy to find 
out what is going on in 
DSTO? 
x  = 5.55  
s = .80 
x  = 6.11 
s  = .78 
2. It would encourage 
people to work together 
rather than compete with 
one another? 
x  = 4.61
s  = 1.16 
x = 4.58 
s  = .88 
3. It would encourage 
people to share the 
information they have, 
or their knowledge and 
expertise?  
x  = 4.83  
s = 1.07 
x  = 4.67 
s  = .87 
 The most interesting insight from Table 3 is the 
different perceptions respondents had of the role 
ARMS could play in lowering the cost of sharing 
within the organisation (Question 1), versus the ability 
of ARMS (or any information system) to influence the 
behavioural and cultural issues that affect information 
and knowledge sharing within the organisation 
(Questions 2 and 3).  
As discussed previously, almost all of the 
information included within ARMS was drawn from 
existing organisational information systems. As a 
result, users were able to build a basic understanding 
of the past, current and planned work within the 
organisation, and the skills, interests and experience of 
staff without requiring the staff described by ARMS to 
actively make the information available. The strong 
results for Question 1 across both studies, and the 
positive insights gathered by the semi-structured 
interviews, show that, within the trial organisation, the 
approach of populating ARMS with existing corporate 
information did lower one of the barriers to sharing by 
providing a low cost method for 'finding out what is 
going on in the organisation'. 
However, the semi-structured interviews 
undertaken as part of Study Five revealed that, while 
being able to find out what was going on in the 
organisation is a necessary first step towards 
supporting sharing and collaboration, by itself, it isn't 
sufficient [14] to significantly impact existing 
behavioural and cultural issues affecting sharing within 
DSTO. Many factors affect sharing, including: 
organisational incentives (for example, rewards, time, 
encouragement) and disincentives; individual 
behaviours; the need to derive value from sharing and 
collaborating; organisational structures; and 
administrative barriers. 
6. Conclusions  
This paper has described ARMS, an information 
system aimed at supporting knowledge and expertise 
finding within DSTO. ARMS was trialled within two 
divisions of DSTO over a six month period. During 
this time, five different studies were undertaken; this 
paper has reported on two of the key studies aimed at 
measuring the utility of ARMS from the perspective of 
R&D staff.  
The work described in this paper has three key 
implications. The first is that, within the trial 
organisation, ARMS provided considerable value in 
supporting the information and knowledge 
management needs of R&D staff. In particular, ARMS 
provided a mechanism by which R&D staff could find 
colleagues that had similar research interests, or had 
expertise or insights that could help with a particular 
problem. ARMS also provided a rich corporate 
memory function, allowing R&D staff to browse the 
organisation’s previous work. As discussed in the 
literature (see Section 2), finding staff, and finding 
previous work are two key information and knowledge 
needs for staff within industrial R&D organisations.  
The rich interlinking of the information held by 
ARMS helped to improve the usability of the 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
information held by providing multiple entry points 
into the information, and intuitive navigations paths 
that matched the user's model of the domain. The rich 
interlinking also allowed users to see the information 
held by ARMS in a wider context, and this often 
enabled them to build a much richer understanding of 
the information. 
By reusing existing corporate information, and not 
requiring R&D staff to add information to ARMS to 
expose their skills, expertise or experience, or to 
expose the past and current work within the 
organisation, ARMS helped overcome one of the key 
cultural and behavioural barriers toward sharing within 
DSTO. While ARMS provided the necessary first step 
towards improving such sharing, ARMS (or any IT 
system) is still likely to have a limited impact on 
entrenched organisational cultural and behavioural 
barriers [26].  
6.1. Limitations 
The work described in this paper has been applied 
only within one R&D organisation. While much of the 
data collected suggests that the information and 
knowledge seeking needs of DSTO's scientists and 
engineers is similar to other R&D organisations 
reported in the literature, the information and 
knowledge management problems faced by DSTO 
may be relatively unique, and as a result, the value of 
an information system like ARMS to other R&D 
organisations may be different.  
A second key limitation of this work was the scope 
of the information made available within ARMS over 
the trial period. As discussed previously, only the 
information related to the two trial divisions was made 
available within ARMS. The data collected as part of 
the studies showed that, in many ways, this limited the 
overall utility of ARMS, especially in supporting 
information seeking outside of the two divisions.  
6.2. Future Work  
A significant feature the current implementation of 
ARMS lacks is automatically generated, easy to 
navigate descriptions that describe the skills, expertise 
and experience of individual staff. While ARMS users 
were generally able to infer and derive the likely skills, 
expertise and experience of individual staff (as 
described Section 5.3), the lack of automatically 
derived descriptions of skills, expertise and experience 
of individual staff limited the ability of users to 
directly search and browse for staff by skills, expertise 
and experience. Previous work [27, 28] has shown 
positive results in deriving descriptions of individual's 
expertise from descriptions of the work they perform, 
or the roles they hold. Given that ARMS already 
strongly relates well described projects and outputs to 
individual staff, it would be possible to automatically 
derive or infer descriptions of individual staff expertise 
from the information already contained within ARMS. 
By drawing together staff, projects and outputs, 
Theme pages act as a potential community of practice 
hub because they provide wide awareness of an 
individual's particular expertise or interests, and 
provide resources relevant to that particular area 
(projects and outputs and their descriptions). 
Potentially, the Theme home pages could be expanded 
to include additional functions to encourage the 
creation of community – for example, via richer 
resources, and richer interaction methods (cf. [14]). 
However, due to time constraints imposed on the 
development of the ARMS prototype, this approach 
has not yet been explored.  
7. Acknowledgements  
The author acknowledges the valuable assistance of 
Dr Mark Burnett, Mr Chris Chapman, Ms Phuong La, 
and Ms Jemma Nguon, in developing the ARMS 
prototype, and further acknowledges the assistance of 
Mr Justin Fidock with some of the evaluation studies. 
8. References 
[1] T. J. Allen, Managing the Flow of Technology: Transfer 
and the Dissemination of Technology Information within the 
R&D Organisation. Cambridge, Mass: MIT Press, 1977. 
[2] S. Hirsh and J. Dinkelacker, "Seeking Information in 
Order to Produce Information: An Empirical Study at 
Hewlett Packard Labs," Journal of the American Society for 
Information Science and Technology, vol. 55, pp. 807–817, 
2004. 
[3] L. Argote and P. Ingram, "Knowledge Transfer: A basis 
for Competitive Advantage in Firms," Organisational
Behaviour and Human Decision Processes, vol. 82, pp. 150–
169, 2000. 
[4] R. Cowan, P. A. David, and D. Foray, "The Explicit 
Economics of Knowledge Codification and Tacitness," 
Industrial and Corporate Change, vol. 9, pp. 211–253, 2000. 
[5] W. H. Starbuck, "Learning by Knowledge-Intensive 
Firms," Journal of Management Studies, vol. 29, pp. 713–
740, 1992. 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
[6] T. Davenport and L. Prusak, Working Knowledge: How 
Organisations Manage What They Know. Boston: Harvard 
Business School Press, 1998. 
[7] J. L. Cummings and B. S. Teng, "Transferring R&D 
knowledge: the key factors affecting knowledge transfer 
success," Journal of Engineering and Technology 
Management vol. 20, pp. 39–68, 2003. 
[8] M. Hertzum and A. M. Pejtersen, "The information-
seeking practices of engineers: Searching for documents as 
well as people," Information Processing and Management,
vol. 36, pp. 761–778, 2000. 
[9] H. Baetjer, "Capital as Embodied Knowledge: Some 
Implications for the Theory of Economic Growth," Review of 
Austrian Economics, vol. 13, pp. 147–174, 2000. 
[10] B. Longueville, J. S. L. Cardinal, J.-C. Bocquet, and P. 
Daneau, "Toward a Project Memory for Innovative Product 
Design. A Decision Making Model," presented at 
International Conference on Engineering Design (ICED03), 
Stockholm, Sweden, 2003. 
[11] B. T. Keane and R. M. Mason, "On the Nature of 
Knowledge – Rethinking Popular Assumptions," presented at 
Thirty-Ninth Annual Hawaii International Conference on 
System Science (HICSS-39), Waikoloa, Hawaii, 2006. 
[12] R. M. Casselman and D. Samson, "Moving Beyond 
Tacit and Explicit: Four Dimensions of Knowledge," 
presented at Thirty-Eighth Annual Hawaii International 
Conference on System Science (HICSS-38), Waikoloa, 
Hawaii, 2005. 
[13] M. Alavi and D. E. Leidner, "Review: Knowledge 
Management and Knowledge Management Systems: 
Conceptual foundations and Research Issue," MIS Quarterly,
vol. 25, pp. 107–135, 2001. 
[14] A. Agostini, S. Albolino, R. Boselli, G. D. Michelis, F. 
D. Paoli, and R. Dondi, "Stimulating Knowledge Discovery 
and Sharing," presented at International Conference on 
Supporting Group Work (GROUP03), Sanibel Island, 
Florida, 2003. 
[15] R. Cross, R. E. Rice, and A. Parker, "Information 
Seeking in Social Context: Structural Influences and Receipt 
of Information Benefits," IEEE Transactions on Systems, 
Man and Cybernetics – Part C: Applications, vol. 11, pp. 
438–448, 2001. 
[16] D. Yimam-Seid and A. Kobsa, "Expert Finding Systems 
for Organizations: Problems and Domain Analysis and the 
DEMOIR approach," Journal of Organizational Computing 
and Electronic Commerce, vol. 13, pp. 1–24, 2003. 
[17] J. J. Berends, "Knowledge Sharing and distributed 
cognition in industrial research," presented at Third 
European Conference on Organisational Knowledge, 
Learning and Capabilities (OKLC02), Athens, Greece, 2002. 
[18] D. W. McDonald and M. S. Ackerman, "Just Talk to 
Me: A Field Study of Expertise Location," presented at ACM 
Conference on Computer Supported Collaborative Work 
(CSCW'98), Seattle, WA, 1998. 
[19] J. Grudin, "Enterprise Knowledge Management and 
Emerging Technologies," presented at Thirty-Ninth Annual 
Hawaii International Conference on Systems Science 
(HICSS-39), Waikoloa, Hawaii, 2006. 
[20] "DEFTEST: Defence Technological and Scientific 
Thesaurus," Defence Reports Section, Department of 
Defence, Canberra, Australia. 1988. 
[21] F. Curbera, M. Duftler, R. Khalaf, W. Nagy, N. Mukhi, 
and S. Weerawarana, "Unravelling the Web Services Web 
An Introduction to SOAP, WSDL, and UDDI," IEEE
Internet Computing, vol. March–April, pp. 86–93, 2002. 
[22] P. Prekop, P. La, M. Burnett, and C. Chapman, "Using 
Web Services to Enable Reuse of DSTO Corporate Data 
within Microsoft Sharepoint: A Case Study," Defence 
Science and Technology Organisation (DSTO), Edinburgh, 
South Australia, tech note DSTO-TN-0672, July 2005. 
[23] E. Rahm and H. H. Do, "Data Cleaning: Problems and 
Current Approaches," IEEE Data Engineering Bulletin, vol. 
23, pp. 3–13, 2000. 
[24] P. Prekop, M. Burnett, and C. Chapman, "The Prototype 
Automated Research Management System (ARMS)," 
Defence Science and Technology Organisation (DSTO), 
Edinburgh, South Australia, technical note DSTO-TN-0540, 
February 2004. 
[25] A. Foster and N. Ford, "Serendipity and Information 
Seeking: an Empirical Study," Journal of Documentation,
vol. 59, pp. 321–340, 2003. 
[26] J. Swan, S. Newell, and M. Robertson, "Knowledge 
Management – When will people management enter the 
debate?" presented at Thirty-Third Annual Hawaii 
International Conference on System Science (HICSS-33), 
Waikoloa, Hawaii, 2000. 
[27] M. Maybury, R. D. Amore, and D. House, "Awareness 
of Organisation Expertise," International Journal of Human-
Computer Interaction, vol. 14, pp. 199–217, 2002. 
[28] A. Vivacquan and H. Lieberman, "Agents to Assist in 
Finding Help," presented at ACM Conference on Human 
Factors in Computing Systems (CHI2000), 2000. 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
	Select a link below
	Return to Main Menu
	Return to Previous View

  This paper reports on work aimed at supporting knowledge and expertise
finding within a large Research and Development (R&D) organisation. The paper
first discusses the nature of knowledge important to R&D organisations and
presents a prototype information system developed to support knowledge and
expertise finding. The paper then discusses a trial of the system within an R&D
organisation, the implications and limitations of the trial, and discusses
future research questions.

Introduction 
This paper describes work undertaken to support 
knowledge and expertise finding within Australia's 
Defence Science and Technology Organisation 
(DSTO). DSTO is a government funded research and 
development (R&D) organisation, with a very broad, 
applied R&D program focused primarily within the 
defence and national security domains. DSTO employs 
approximately 1900 engineers and scientists across a 
wide range of academic disciplines (about 30% of staff 
hold PhDs), within seven sites throughout Australia. 
Like most other large R&D organisations [1, 2] and 
professional services firms, DSTO is a project-centric 
organisation; projects are formed to address specific 
questions or problems, or to develop specific products. 
The nature of the outcomes of the projects undertaken 
by DSTO varies considerably, and can range from 
academic papers and technical reports, through to 
prototype and working system development, and to 
professional services and consulting engagements.  
The work described in this paper is part of an 
ongoing knowledge management improvement 
program aimed at exploring:  
Methods to allow staff to build and maintain 
wide and detailed awareness of DSTO's past, 
current and planned projects; 
Methods to enable staff to locate other staff 
with relevant skills, interests, abilities or 
experience; 
Low cost (in terms of time and effort) methods 
to support the development of communities of 
interest, and less-formal collaboration and  
sharing within the organisation;  
Organisational cultural and behavioural issues 
that may act as barriers to effective knowledge 
and expertise sharing.  
A prototype information system, the Automated 
Research Management System (ARMS), was 
developed to explore approaches to addressing these 
issues.  
Section 2 discusses the nature of knowledge and 
knowledge management within the R&D environment, 
and describes the types of support for knowledge and 
expertise-finding needed within organisations such as 
DSTO. Section 3 describes ARMS and how it supports 
knowledge and expertise finding within DSTO. 
Section 4 outlines the ARMS trial and trial 
methodology, and Section 5 discusses the results of 
two studies undertaken as part of the ARMS trial. 
Finally, Section 6 discusses the implications and 
limitations of the work undertaken so far and describes 
potential areas for future work. 
2. The Nature of Knowledge and Expertise 
within R&D Organisations  
2.1. Theoretical Background  
The main theoretical idea underpinning this work is 
that the knowledge important to an organisation, or 
that makes it unique or gives it a competitive 
advantage, is embedded in key elements that make up 
the organisation [3–5]. 
 According to [3], this knowledge is embedded in 
three key organisational elements – the members of the 
organisation, the tools used within the organisation, 
and the tasks performed by the organisation. For many 
©1530-1605/07 $20.00     Commonwealth of Australia 2007
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
organisations, organisationally important knowledge is 
embedded within skills, experiences, expertise and 
competencies of the individuals that make up the 
organisation [1, 3, 6]. This is particularly true for R&D 
organisations [1] and other professional services 
organisations [5]. As well as people, significant 
organisational knowledge is embedded within the tools 
the organisation uses, including specialised physical 
hardware used as part of a manufacturing process, for 
example, through to conceptual or intellectual tools 
such as consulting or analysis frameworks [3, 7]. The 
third key element identified by [3] is the tasks 
performed by the organisation. Tasks reflect an 
organisation's goals, intention and purpose [3, 5, 7].  
For R&D, engineering and other professional 
services organisations, key knowledge is also 
embedded within the products or other kinds of 
outcome the organisation produces. The development 
of products or other kinds of outcome uniquely 
combines together the organisation's staff, tools and 
tasks to address a particular question or problem, or to 
develop some kind of product, and can be seen as 
uniquely embedding the application of the 
organisation's collective knowledge, skills, experiences 
and expertise within a particular domain, to address a 
particular question or problem or to develop some kind 
of product [1, 7,  9, 10]. 
As discussed in [11], knowledge management is 
centred on two, potentially limiting, philosophical 
foundations. The first is the idea that tacit and explicit 
knowledge are two distinctly different forms or types 
of knowledge, rather than simply being a dimension 
along which all knowledge exists. The underlying 
assumption that tacit and explicit knowledge are 
different leads to the conclusion that a key goal of 
knowledge management is the codification of tacit 
knowledge into explicit knowledge [12]. However, as 
[11] points out, not all knowledge can (or should) be 
codified, and any knowledge management approaches 
that rely on the codification of knowledge are likely to 
fail. The second philosophical foundation that 
knowledge management rests on is the data – 
information – knowledge continuum: the idea that 
information is in some way better data and that 
knowledge is in some way better information. As 
discussed in [11], this leads to knowledge management 
approaches that focus only on capturing and managing 
some form of codified knowledge [13], while ignoring 
data and information that could provide equal or even 
greater value to users.  
However, the view that knowledge important to an 
organisation is embedded in the key elements that 
make up the organisation potentially provides an 
approach to knowledge management that doesn't rest 
on these two potentially limiting foundations. The 
focus of a knowledge management approach that 
accepts the embeddedness of knowledge as important 
becomes one of finding and devising methods, systems 
and approaches that in some way index and expose the 
core entities within the organisation that hold the 
embedded knowledge, rather than focusing on 
codification and managing the codified knowledge. 
The importance of the knowledge embedded in the 
different organisational entities will vary with the 
nature of the organisation and the nature of the work 
performed by the organisation. The following section 
discusses the kinds of knowledge important within an 
industrial R&D organisation, and the kinds of entities 
the knowledge is likely to be embedded within.  
2.2. Knowledge and Expertise within R&D 
Organisations 
For industrial R&D organisations (and many other 
knowledge intensive firms [14]), the key knowledge 
that makes the organisation unique is embedded in the 
experience, expertise and competencies of the 
engineers and scientists that make up the organisation 
and the organisation's collective project history – the 
past products the organisation has developed, or the 
past problems or questions it has addressed [1, 10]. 
 Within most industrial R&D organisations, the 
products developed or projects undertaken require the 
application of collective individual knowledge, skills, 
experience and expertise in unique ways. As a result, 
products and projects can be seen as an embedding the 
application of the organisation's collective knowledge, 
skills, experience and expertise, within a particular 
domain, to address a particular question or problem [1, 
The information and knowledge created as part of 
past projects can provide important insights into 
finding solutions to current problems, or gaining an 
understanding of how similar problems have been 
solved in the past. Project histories can also support 
problem reformulation, and can offer some help in 
validating proposed solutions [15]. Past projects are 
also important because they can provide a link back to 
the staff who contributed. This in turn can provide 
valuable insight into the knowledge, experience and 
expertise that individual staff may have [1, 8, 15]. 
Colleagues act not only as important sources of 
information, and pointers to other sources of 
information [16], but most importantly they provide an 
interactive think along function [17, 18]. 
As discussed previously, the goal of this work was 
to develop ways of improving knowledge and 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
expertise finding. The approach taken by the work 
described in the following section was to develop an 
information system (ARMS) to act as a rich, 
interactive model of the embedded knowledge within 
the organisation – in particular, the current and past 
projects within the organisation, and the expertise, 
skills and experience of the staff that make up the 
organisation.  
3. The ARMS Prototype 
The Automated Research Management System 
(ARMS) is a prototype, web based, information system 
developed to explore approaches to supporting 
knowledge and expertise finding within DSTO.  
ARMS holds information about the key R&D 
entities relevant to DSTO – staff and projects, as well 
as formal and informal project outputs (academic 
papers, technical reports, design documents, data 
collections, and so on). Within ARMS, these entities 
are organised around the organisation's hierarchical 
structure, and around Themes – collections of 
taxonomic descriptors used to describe the client and 
scientific domains that DSTO works within. The key 
R&D entities and their relationships are shown in 
Figure 1.  
StaffStaff
ThemeTheme
ProjectProject
OutputOutput
Author Of
Created ForDescribed By
Manager Of
Contributes To
UnitUnitSiteSite
Described By
Interested In
Located At
Exists In
Exists In
Role In
Head Of
Role InMember Of
Figure 1. Conceptual domain model 
3.1. The Users' Perspective  
As discussed in Section 2, the key knowledge, 
experience, expertise and competency of an industrial 
R&D organisation exists in its project history and the 
collective expertise, skills, experience and knowledge 
of its scientists and engineers. ARMS supports 
knowledge and expertise finding by exposing both 
projects (and the outputs associated with projects) and 
staff as richly interlinked first class objects (unlike 
many similar systems [19], where staff are either not 
included, or simply included as author labels 
associated with the entities held by the system).  
From a user’s perspective, ARMS exists as a 
collection of dynamically generated web pages, with 
each R&D entity having its own web page that pulls 
together all the information related to that entity.  
Staff pages contain basic staff information, 
including: contact information, site location and 
organisational affiliation, and descriptions and links to 
the projects and project outputs the staff member has 
contributed to. This information is drawn from existing 
organisational information systems. Staff pages can 
also contain optional information, including staff 
descriptions of current and past project work, staff 
photo and basic biographical information, and current 
interests and work.  
Project pages contain project descriptions (abstract, 
overview, background, themes, etc), project 
milestones, planned deliverables, information about the 
project's relationship to other past current and planned 
projects, and the project's status. Project pages also 
contain information and links to the staff that have 
contributed to the project, as well as information about 
and links to the project's outputs. All this information 
is obtained from existing information systems. End 
users are also able to add richer project description 
information, as well as any kinds of additional outputs 
to the project's home page.  
Output pages contain basic metadata describing the 
output (title, abstract, publication details, document 
type and so on) and the documents that make up the 
output (for example MS-Word files, data files, image 
files, etc). Output pages contain information and links 
to the staff who contributed to the output, as well as 
information and links to the project the output was 
developed for. Output information contained within 
ARMS is extracted from an existing publication 
management system. End users are also able to add 
additional project outputs to any project page.  
ARMS provides multiple entry points into the data. 
Users can access staff, project and output entities 
directly via the staff, project and output browsing 
pages. Each of these pages provides filterable lists of 
the staff, project and output entities contained within 
ARMS.  
In addition to the browsing pages, users can also 
enter ARMS via a representation of the organisation's 
structure (the Unit entities shown in Figure 1), or via 
descriptions of the organisation's R&D program (the 
Themes entities shown in Figure 1). 
Each DSTO unit has a corresponding unit page that 
holds basic contact information for the unit, the unit’s 
head, administrative contacts for the unit, and the 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
unit’s structure (for example the groups within a 
branch), the staff who are members of that unit, and 
the tasks associated with that unit. This information is 
extracted from several different existing information 
systems. In addition, end users can optionally insert 
additional information describing units. Units can be 
directly entered via the unit browse page, an 
interactive form of the organisation’s hierarchical 
structure.  
DSTO's R&D program is represented as a 
collection of Themes, a combination of taxonomy 
descriptors (based around DEFTEST [20]) that cover 
the core science and technology (S&T) areas of DSTO, 
and taxonomy descriptors that cover the key client 
areas DSTO works within. S&T and client Themes are 
used to describe the staff, project and output 
information held within ARMS. Each Theme has an 
automatically generated home page that aggregates all 
the projects, staff and outputs described by the Theme. 
In many ways the Theme home pages can be seen as 
aggregating everything the organisation 'knows' about 
a particular Theme area – that is all the staff, projects 
and outputs related to that Theme. Themes can be 
directly entered via the Theme browse page, an 
interactive and structured collection of the Themes 
held by ARMS.  
An important part of ARMS is rich hyperlinking 
between the various entities that contextualises the 
information held by ARMS. Staff, for example, are 
contextualised by the projects they contribute (or have 
contributed) to and the outputs they have contributed 
to. Projects are contextualised by the staff that 
contribute to them, and the part of the organisation 
they were performed by. The intent of the 
contextualisation is to allow users to infer richer 
meanings based on explicit relationships present in the 
data. The utility of this approach, especially in terms of 
expertise finding, is discussed in Section 5.2.  
In addition to the browsing functions, ARMS also 
includes a search function that support free text 
searching over all the information held by ARMS 
(fields associated with each entity as well as full 
document text), field searching (for example, searching 
explicitly by unit name, or project number), as well as 
Theme searching. The different search types can be 
combined with Boolean operators (AND and OR) to 
form complex queries.  
3.2. Technical Perspective 
From a technical perspective, ARMS consists of a 
centralised repository that holds information extracted 
from existing corporate information systems, as well as 
a small amount of information specifically created to 
support ARMS. Access to the information and 
functions provided by ARMS is via a web based 
interface that supports the functions described in 
Section 3.1, as well as a Web Services1 interface that 
provides dynamic programmatic access to the core 
functions and information (see Figure 2). The Web 
Services interface was provided to support dynamic 
access to by ARMS by specialised applications, such 
as collaborative Microsoft SharePoint portals [22] and 
other specialised web sites. 
Taxonomy
Repository
Domain Logic 
Unstructured 
Repository 
Sem i-structured 
Repository 
Search Engine
W eb Interface W eb Services Interface
Staff 
System
Project 
Management
System
Publication 
Repository
W rapperW rapper W rapper
Collaborative 
SharePoint 
Collaborative 
SharePoint 
Collaborative 
SharePoint 
Portal
Specialised 
W eb Sites 
S&T Staff M anager
Figure 2. ARMS logical architecture  
Almost all of the information used by ARMS was 
drawn from existing corporate information systems as 
well as less-formal corporate information collections 
such as spreadsheets and intranet sites. The re-used 
corporate information generally needed to be cleaned 
and reformatted before it could be used. Most of the 
data cleaning issues encountered were common to 
other data warehousing projects [23], and included a 
lack of common record identifiers across the different 
systems, conflicting data values across the different 
systems, missing and erroneous data, and name and 
type conflicts. The data cleaning and re-formatting 
functions were encapsulated in series of customer 
wrappers that were developed for each of the core 
corporate applications and other corporate information 
sources.  
As discussed in Section 3.1, ARMS provided a 
search function over all the information held. The 
search engine combined free text searching over the 
unstructured information held by ARMS (generally 
reports, papers, presentation and other project 
outcomes) with searching over the structured and 
                                                          
1 An overview of Web Services can be found in [21]. 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
semi-structured information held. Free text search was 
provided by a commercially available text search 
engine; searching over structured and semi-structured 
information was provided by a custom developed 
search engine.  
4. The ARMS Trial  
To validate the concepts underpinning ARMS and 
its utility in supporting expertise and knowledge 
finding within DSTO, a prototype version of the 
system was evaluated within two DSTO divisions from 
June until November 2004.  
 The ARMS prototype (as described in [24]) was 
fully developed and fully populated with relevant staff, 
project and output information. The data held by 
ARMS was kept up-to-date throughout the trial period. 
During the trial period ARMS was made available to 
staff within the two divisions selected (Division A and 
Division B). These divisions were selected because 
together they reflect a good cross section of the staff, 
organisational structures, and research programs within 
DSTO. 
Division A was split across five sites, and generally 
had a multi-disciplinary, professional services focus. 
At the time of the trial it contained approximately 80 
staff. 
Division B was split across three sites, and 
generally had a computer science/software engineering 
base, with a strong R&D focus. At the time of the trial, 
it contained approximately 110 staff.  
Table 1. ARMS Trial Studies 
Study Date Collection Methods 
Study One: 
Organisational Wide 
Focus Groups 
May  Focus Groups  
Study Two: Stake-
holder Interviews 
June/July  Unstructured and 
semi-structured 
interviews 
Study Three: Concept 
and Implementation 
Survey 
July  Survey questions  
Study Four: Project 
Seeking Experiment  
September  Data seeking 
experiment
Study Five: Utility of 
Usage Study 
November  Semi-structured 
interviews and 
survey questions 
Over the trial period, five different studies were 
undertaken (see Table 1). Each of the studies aimed to 
explore the utility of ARMS from various perspectives. 
This paper discusses two of the key studies, Study 
Three and Study Five, both of which measured the 
utility of ARMS from the perspective of R&D staff. 
The Concept and Implementation Survey (Study 
Three) was undertaken after ARMS had been available 
in the two trial divisions for approximately 1½ months. 
The survey was sent to all staff in the two trial 
divisions who had used ARMS at least once during the 
trial period. Of the 75 divisional staff who had used 
ARMS at least once, 23 staff responded.  
Respondents were spread across three sites; 40% 
from Site A, 52% from Site B, and 8% from Site C. 
The respondents represented a good cross section of 
the organisation’s management structure, with 17% of 
respondents holding organisational unit management 
positions (group or branch), and 40% of respondents 
having project management responsibility. Overall the 
sample reflected a good cross-section of the 
organisation, with slight over-sampling of respondents 
from Site B. The results of this study are discussed in 
Section 5.  
The Utility of Usage Study (Study Five) was 
undertaken towards the end of the ARMS trial. The 
most frequent users of ARMS were identified, and 
invited to participate in the study. Of the users 
selected, 10 agreed to participate. Study respondents 
were spread across three sites; 70% from Site A, 20% 
from Site B and 10% from Site C. Around half of the 
respondents held project management responsibility. 
Overall, the sample in this study reflected a good cross 
section of the organisation, except for the lack of 
respondents holding organisational unit management 
positions, and an over-sampling of respondents from 
Site A.  
Study Five was run as a set of survey questions and 
semi-structured interviews. Participants were asked to 
list the functions they used ARMS to perform over the 
previous month. The interviews discussed how 
participants used ARMS, its utility to them, and how 
ARMS compared to alternative approaches they may 
have tried. The interviews were recorded, and the 
recordings transcribed. The interviews ran for an 
average of 50 minutes each.  
5. Results and Discussion  
5.1. ARMS Usage  
Studies Three and Five recorded how ARMS was 
used by participants over the previous month. Table 2, 
below, lists the functions ARMS was used to perform 
and the percentage of users who used ARMS to 
perform the listed function.  
One of the most striking features of Table 2 is the 
dramatic changes in some of the ways ARMS was used 
over the trial period. (Study Three was undertaken 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
after ARMS had been available for 1½ months; Study 
Five was undertaken toward the end of the 6 month 
trial).
Table 2. Overall ARMS Usage 
Use of ARMS  
Study
Three 
Study
Five 
1. Exploring the system; for example 
seeing what information it holds and 
exploring how I could use it. 
96% 20% 
2. Building an awareness of DSTO's 
projects and staff; for example seeing 
what work a group is doing, or what 
work is going on within a Work Area, 
or seeing what a particular person has 
been working on recently. 
61% 10% 
3. Finding out about a particular project; 
for example who is working on it, 
who the project manager is, or finding 
the various papers, reports and other 
outputs associated with the project.  
56% 40% 
4. Finding out about a person; for 
example their contact information, 
what they are working on, what 
papers and reports they have written 
or what outputs they have been 
involved in, or finding out about their 
interests or experience. 
52% 50% 
5. Finding out about a particular 
research area; for example who is 
interested in the area, which tasks 
contribute to the research area, or 
what work has been produced in the 
research area.  
35% 20% 
6. Finding out about a particular 
organisational unit; for example who 
is in the unit, what tasks the unit is 
responsible for, or finding contact 
information for the unit.  
30% 0% 
7. Finding out about a particular Client 
Area; for example who is working in, 
which tasks contribute to the Client 
Area, or what work has been 
produced for the Client Area.  
22% 0% 
8. Finding a particular formal or 
informal output.
13% 30% 
9. Searching for a person with particular 
skills, interests, experience or 
abilities. 
13% 10% 
10. Other 4% 0% 
The largest change was the drop in exploratory use 
of ARMS between the two studies. This change is 
likely to be a result of users building an understanding 
of the functions and features of ARMS as the study 
progressed. Once an understanding of ARMS (based 
on exploring the system) was developed they either 
moved to non-use, if they felt ARMS provided no 
value, or they moved to using specific features and 
functions of ARMS.  
Using ARMS to build and maintain an awareness of 
the work being performed within DSTO (Question 2), 
and using ARMS to find out about particular 
organisational units (Question 6) and client areas 
(Question 7) also dropped over the study period. The 
drop in using ARMS to perform these functions is 
likely to be due to two key factors. The semi-structured 
interviews undertaken as part of Study Five revealed 
that awareness (Question 2) is something that is built 
for a specific purpose, for example moving to a new 
organisational unit or moving into a new research area, 
and then is maintained by actively being involved in 
the organisational unit or research area. Once an 
overall awareness has been built, it is maintained by 
more direct information seeking activities, for example 
finding out about a staff member, or a project, or 
hunting up specific project outputs. 
The changes in the results for Questions 6 and 7 are 
due to similar reasons, with participants initially using 
ARMS to build a general awareness of organisational 
units and client areas, and then maintaining that 
awareness by more direct information seeking 
methods.  
The second factor likely to affect the use of ARMS 
to build and maintain awareness is a fundamental limit 
inherent within the ARMS trial. As discussed 
previously, ARMS was fully populated and maintained 
with data drawn from only two trial divisions, not the 
whole organisation. As a result the value of ARMS in 
providing a rich awareness to participants was limited 
to only the two trial divisions. This limitation and its 
likely impact on the overall trial result is discussed in 
more detail in Section 6.1.  
5.2. Finding People, Projects and Outputs 
One set of functions that ARMS was regularly used 
to perform over the study period was finding people, 
projects and outputs (Questions 3, 4 and 8 
respectively).  
As shown by Table 2, finding people was 
constantly the most frequently used function of 
ARMS. The semi-structured interviews undertaken as 
part of Study Five revealed that participants who used 
ARMS to find people were generally looking for 
colleagues who have similar research interests or 
worked in similar areas. The semi-structured 
interviews revealed that users who employed ARMS in 
this way, were, in general, seeking out connections 
with other colleagues in order to share ideas, discuss 
problems/issues, have work reviewed, gain insights and 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
different perspectives on a common/shared problems, 
and perhaps even re-use and build on the work already 
completed by colleagues.  
These findings confirm much of the previous 
research (described in Section 2) describing the 
information seeking behaviours and needs of scientists 
and engineers.  
Overall, participants reported that the information 
available via ARMS in most cases allowed them to 
identify colleagues they felt would be worth 
contacting. However, there were some limitations in 
the data ARMS provided. One key limitation was the 
scope of the information available via ARMS. As 
discussed previously, ARMS only contained 
information for the two trial divisions. This limited the 
number of colleagues users could learn about. The 
second key limitation with the staff data available via 
ARMS was that individual staff expertise, experience 
and research interests, in general, could only be 
derived by understanding the context surrounding the 
staff member (this is discussed in Section 5.3). While 
in most cases the surrounding context provided users 
with sufficient information to infer a staff member's 
expertise, experience and research interests, it did 
mean that users were generally unable to directly 
search for staff with particular expertise, experience 
and research interests but instead had to search for staff 
via outputs or projects, and infer staff relevance based 
on the relationships between staff and the projects and 
outputs they contributed to.  
An interesting question explored as part of the semi-
structured interviews was the relationship between the 
kinds of information available via ARMS, and the 
kinds of information available via the user's social 
networks. Most respondents acknowledged the 
importance their social network plays in being able to 
find colleagues with specific expertise, experience and 
research interests. However, they generally found that 
there were limits in the coverage of their social 
network; in particular many respondents felt their 
social network often didn't extend into other 
organisational sites, or into other organisational units. 
In comparison, they felt that the information held by 
ARMS was more complete and offered greater 
coverage of all parts of the organisation, and they 
viewed ARMS as a way of filling gaps in their social 
network.  
The second key group of functions regularly used 
throughout the trial was finding projects and outputs. 
The semi-structured interviews revealed that when 
searching for projects and outputs, participants were 
generally looking either to find relevant and useful 
colleagues, or were explicitly looking for information 
related to past projects. As discussed in Section 2, for 
engineers in particular, descriptions of past projects 
and the formal and informal products of projects 
(reports, papers, designs, data sets, meeting minutes, 
and so on) are important because they offer insights 
and approaches to solving past problems that may be 
useful for solving current problems. The semi-
structured interviews revealed that participants were 
using ARMS in this way.  
As well as ARMS, participants could obtain project 
and formal publication information from two existing 
organisational information systems, a Project 
Management System (PMS) and a Publications 
Repository (PR). The basic project and publication 
information held by ARMS was obtained from these 
two systems, and in most cases ARMS held little 
additional information. The key value added by ARMS 
was the rich contextualisation of the information, 
together with existing staff information to provide 
multiple entry point into the information sought, and to 
allow rich relationships and deeper meaning of the 
information to be inferred. This is discussed in more 
detail in the following section.  
5.3. Contextualisation and Navigation  
As shown by Figure 1, the information contained 
within ARMS is richly interlinked. The rich 
interlinking helps to contextualise this information, 
making it easier for ARMS users to develop a deeper 
understanding of the information held within ARMS, 
as well as providing users with multiple entry points 
into the information, and a navigation model drawn 
from the users' domains.  
The semi-structured interviews undertaken as part 
of Study 5 showed that by knowing, for example, the 
background of key staff involved in a project, users are 
able to infer more about the likely directions, 
perspectives or methodologies a project may use. Or 
by knowing about the project that created a particular 
output, users are able to build a richer understanding of 
the output's meaning.  
The rich interlinking within ARMS also provided 
users with multiple entry points into the information 
held by ARMS, and provided them with natural 
navigation paths drawn from their domain. The semi-
structured interviews revealed that almost all users 
reported that the richly contextualised nature of ARMS 
helped them navigate ARMS, allowing them to enter 
ARMS from many different directions, often using 
only incomplete information as a starting point.  
For example, several users reported browsing 
ARMS by starting with a vague awareness that a staff 
member may be doing work that might be interesting 
or relevant to them. By using ARMS, they were able to 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
move from the individual's staff page to their project 
pages – to find out about the work being performed. 
From project pages, they would move to output pages, 
to gain a deeper insight of the work being performed, 
or they would move to Theme pages, to find related 
work, staff or outputs. Other users reported a similar 
approach using projects as a starting point to find 
people or to find other related projects.  
This form of navigation also allows for far richer 
forms of accidental information discovery [25], and 
provides users with the ability to develop rich mental 
models of the organisation's past, planned and current 
work program.   
5.4. Cultural Impact  
As discussed in Section 1, one of the goals of the 
work described in this paper was to address potential 
barriers to information sharing within DSTO, 
including behavioural and cultural issues, as well as a 
lack of low cost methods, systems or processes to 
support information sharing within DSTO. Both Study 
Three and Study Five attempted to measure the likely 
influence ARMS would have on these barriers. The 
results are shown in Table 3; questions were measured 
on a 7 point, end anchored scale, with 1 meaning 
strongly disagree, and 7 meaning strongly agree.
Table 3. Cultural Impact 
Question Study Three Study Five 
If ARMS held information about everyone in the DSTO and 
all DSTO's past, current and planned work: 
1. It would be easy to find 
out what is going on in 
DSTO? 
x  = 5.55  
s = .80 
x  = 6.11 
s  = .78 
2. It would encourage 
people to work together 
rather than compete with 
one another? 
x  = 4.61
s  = 1.16 
x = 4.58 
s  = .88 
3. It would encourage 
people to share the 
information they have, 
or their knowledge and 
expertise?  
x  = 4.83  
s = 1.07 
x  = 4.67 
s  = .87 
 The most interesting insight from Table 3 is the 
different perceptions respondents had of the role 
ARMS could play in lowering the cost of sharing 
within the organisation (Question 1), versus the ability 
of ARMS (or any information system) to influence the 
behavioural and cultural issues that affect information 
and knowledge sharing within the organisation 
(Questions 2 and 3).  
As discussed previously, almost all of the 
information included within ARMS was drawn from 
existing organisational information systems. As a 
result, users were able to build a basic understanding 
of the past, current and planned work within the 
organisation, and the skills, interests and experience of 
staff without requiring the staff described by ARMS to 
actively make the information available. The strong 
results for Question 1 across both studies, and the 
positive insights gathered by the semi-structured 
interviews, show that, within the trial organisation, the 
approach of populating ARMS with existing corporate 
information did lower one of the barriers to sharing by 
providing a low cost method for 'finding out what is 
going on in the organisation'. 
However, the semi-structured interviews 
undertaken as part of Study Five revealed that, while 
being able to find out what was going on in the 
organisation is a necessary first step towards 
supporting sharing and collaboration, by itself, it isn't 
sufficient [14] to significantly impact existing 
behavioural and cultural issues affecting sharing within 
DSTO. Many factors affect sharing, including: 
organisational incentives (for example, rewards, time, 
encouragement) and disincentives; individual 
behaviours; the need to derive value from sharing and 
collaborating; organisational structures; and 
administrative barriers. 
6. Conclusions  
This paper has described ARMS, an information 
system aimed at supporting knowledge and expertise 
finding within DSTO. ARMS was trialled within two 
divisions of DSTO over a six month period. During 
this time, five different studies were undertaken; this 
paper has reported on two of the key studies aimed at 
measuring the utility of ARMS from the perspective of 
R&D staff.  
The work described in this paper has three key 
implications. The first is that, within the trial 
organisation, ARMS provided considerable value in 
supporting the information and knowledge 
management needs of R&D staff. In particular, ARMS 
provided a mechanism by which R&D staff could find 
colleagues that had similar research interests, or had 
expertise or insights that could help with a particular 
problem. ARMS also provided a rich corporate 
memory function, allowing R&D staff to browse the 
organisation’s previous work. As discussed in the 
literature (see Section 2), finding staff, and finding 
previous work are two key information and knowledge 
needs for staff within industrial R&D organisations.  
The rich interlinking of the information held by 
ARMS helped to improve the usability of the 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
information held by providing multiple entry points 
into the information, and intuitive navigations paths 
that matched the user's model of the domain. The rich 
interlinking also allowed users to see the information 
held by ARMS in a wider context, and this often 
enabled them to build a much richer understanding of 
the information. 
By reusing existing corporate information, and not 
requiring R&D staff to add information to ARMS to 
expose their skills, expertise or experience, or to 
expose the past and current work within the 
organisation, ARMS helped overcome one of the key 
cultural and behavioural barriers toward sharing within 
DSTO. While ARMS provided the necessary first step 
towards improving such sharing, ARMS (or any IT 
system) is still likely to have a limited impact on 
entrenched organisational cultural and behavioural 
barriers [26].  
6.1. Limitations 
The work described in this paper has been applied 
only within one R&D organisation. While much of the 
data collected suggests that the information and 
knowledge seeking needs of DSTO's scientists and 
engineers is similar to other R&D organisations 
reported in the literature, the information and 
knowledge management problems faced by DSTO 
may be relatively unique, and as a result, the value of 
an information system like ARMS to other R&D 
organisations may be different.  
A second key limitation of this work was the scope 
of the information made available within ARMS over 
the trial period. As discussed previously, only the 
information related to the two trial divisions was made 
available within ARMS. The data collected as part of 
the studies showed that, in many ways, this limited the 
overall utility of ARMS, especially in supporting 
information seeking outside of the two divisions.  
6.2. Future Work  
A significant feature the current implementation of 
ARMS lacks is automatically generated, easy to 
navigate descriptions that describe the skills, expertise 
and experience of individual staff. While ARMS users 
were generally able to infer and derive the likely skills, 
expertise and experience of individual staff (as 
described Section 5.3), the lack of automatically 
derived descriptions of skills, expertise and experience 
of individual staff limited the ability of users to 
directly search and browse for staff by skills, expertise 
and experience. Previous work [27, 28] has shown 
positive results in deriving descriptions of individual's 
expertise from descriptions of the work they perform, 
or the roles they hold. Given that ARMS already 
strongly relates well described projects and outputs to 
individual staff, it would be possible to automatically 
derive or infer descriptions of individual staff expertise 
from the information already contained within ARMS. 
By drawing together staff, projects and outputs, 
Theme pages act as a potential community of practice 
hub because they provide wide awareness of an 
individual's particular expertise or interests, and 
provide resources relevant to that particular area 
(projects and outputs and their descriptions). 
Potentially, the Theme home pages could be expanded 
to include additional functions to encourage the 
creation of community – for example, via richer 
resources, and richer interaction methods (cf. [14]). 
However, due to time constraints imposed on the 
development of the ARMS prototype, this approach 
has not yet been explored.  
7. Acknowledgements  
The author acknowledges the valuable assistance of 
Dr Mark Burnett, Mr Chris Chapman, Ms Phuong La, 
and Ms Jemma Nguon, in developing the ARMS 
prototype, and further acknowledges the assistance of 
Mr Justin Fidock with some of the evaluation studies. 
8. References 
[1] T. J. Allen, Managing the Flow of Technology: Transfer 
and the Dissemination of Technology Information within the 
R&D Organisation. Cambridge, Mass: MIT Press, 1977. 
[2] S. Hirsh and J. Dinkelacker, "Seeking Information in 
Order to Produce Information: An Empirical Study at 
Hewlett Packard Labs," Journal of the American Society for 
Information Science and Technology, vol. 55, pp. 807–817, 
2004. 
[3] L. Argote and P. Ingram, "Knowledge Transfer: A basis 
for Competitive Advantage in Firms," Organisational
Behaviour and Human Decision Processes, vol. 82, pp. 150–
169, 2000. 
[4] R. Cowan, P. A. David, and D. Foray, "The Explicit 
Economics of Knowledge Codification and Tacitness," 
Industrial and Corporate Change, vol. 9, pp. 211–253, 2000. 
[5] W. H. Starbuck, "Learning by Knowledge-Intensive 
Firms," Journal of Management Studies, vol. 29, pp. 713–
740, 1992. 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
[6] T. Davenport and L. Prusak, Working Knowledge: How 
Organisations Manage What They Know. Boston: Harvard 
Business School Press, 1998. 
[7] J. L. Cummings and B. S. Teng, "Transferring R&D 
knowledge: the key factors affecting knowledge transfer 
success," Journal of Engineering and Technology 
Management vol. 20, pp. 39–68, 2003. 
[8] M. Hertzum and A. M. Pejtersen, "The information-
seeking practices of engineers: Searching for documents as 
well as people," Information Processing and Management,
vol. 36, pp. 761–778, 2000. 
[9] H. Baetjer, "Capital as Embodied Knowledge: Some 
Implications for the Theory of Economic Growth," Review of 
Austrian Economics, vol. 13, pp. 147–174, 2000. 
[10] B. Longueville, J. S. L. Cardinal, J.-C. Bocquet, and P. 
Daneau, "Toward a Project Memory for Innovative Product 
Design. A Decision Making Model," presented at 
International Conference on Engineering Design (ICED03), 
Stockholm, Sweden, 2003. 
[11] B. T. Keane and R. M. Mason, "On the Nature of 
Knowledge – Rethinking Popular Assumptions," presented at 
Thirty-Ninth Annual Hawaii International Conference on 
System Science (HICSS-39), Waikoloa, Hawaii, 2006. 
[12] R. M. Casselman and D. Samson, "Moving Beyond 
Tacit and Explicit: Four Dimensions of Knowledge," 
presented at Thirty-Eighth Annual Hawaii International 
Conference on System Science (HICSS-38), Waikoloa, 
Hawaii, 2005. 
[13] M. Alavi and D. E. Leidner, "Review: Knowledge 
Management and Knowledge Management Systems: 
Conceptual foundations and Research Issue," MIS Quarterly,
vol. 25, pp. 107–135, 2001. 
[14] A. Agostini, S. Albolino, R. Boselli, G. D. Michelis, F. 
D. Paoli, and R. Dondi, "Stimulating Knowledge Discovery 
and Sharing," presented at International Conference on 
Supporting Group Work (GROUP03), Sanibel Island, 
Florida, 2003. 
[15] R. Cross, R. E. Rice, and A. Parker, "Information 
Seeking in Social Context: Structural Influences and Receipt 
of Information Benefits," IEEE Transactions on Systems, 
Man and Cybernetics – Part C: Applications, vol. 11, pp. 
438–448, 2001. 
[16] D. Yimam-Seid and A. Kobsa, "Expert Finding Systems 
for Organizations: Problems and Domain Analysis and the 
DEMOIR approach," Journal of Organizational Computing 
and Electronic Commerce, vol. 13, pp. 1–24, 2003. 
[17] J. J. Berends, "Knowledge Sharing and distributed 
cognition in industrial research," presented at Third 
European Conference on Organisational Knowledge, 
Learning and Capabilities (OKLC02), Athens, Greece, 2002. 
[18] D. W. McDonald and M. S. Ackerman, "Just Talk to 
Me: A Field Study of Expertise Location," presented at ACM 
Conference on Computer Supported Collaborative Work 
(CSCW'98), Seattle, WA, 1998. 
[19] J. Grudin, "Enterprise Knowledge Management and 
Emerging Technologies," presented at Thirty-Ninth Annual 
Hawaii International Conference on Systems Science 
(HICSS-39), Waikoloa, Hawaii, 2006. 
[20] "DEFTEST: Defence Technological and Scientific 
Thesaurus," Defence Reports Section, Department of 
Defence, Canberra, Australia. 1988. 
[21] F. Curbera, M. Duftler, R. Khalaf, W. Nagy, N. Mukhi, 
and S. Weerawarana, "Unravelling the Web Services Web 
An Introduction to SOAP, WSDL, and UDDI," IEEE
Internet Computing, vol. March–April, pp. 86–93, 2002. 
[22] P. Prekop, P. La, M. Burnett, and C. Chapman, "Using 
Web Services to Enable Reuse of DSTO Corporate Data 
within Microsoft Sharepoint: A Case Study," Defence 
Science and Technology Organisation (DSTO), Edinburgh, 
South Australia, tech note DSTO-TN-0672, July 2005. 
[23] E. Rahm and H. H. Do, "Data Cleaning: Problems and 
Current Approaches," IEEE Data Engineering Bulletin, vol. 
23, pp. 3–13, 2000. 
[24] P. Prekop, M. Burnett, and C. Chapman, "The Prototype 
Automated Research Management System (ARMS)," 
Defence Science and Technology Organisation (DSTO), 
Edinburgh, South Australia, technical note DSTO-TN-0540, 
February 2004. 
[25] A. Foster and N. Ford, "Serendipity and Information 
Seeking: an Empirical Study," Journal of Documentation,
vol. 59, pp. 321–340, 2003. 
[26] J. Swan, S. Newell, and M. Robertson, "Knowledge 
Management – When will people management enter the 
debate?" presented at Thirty-Third Annual Hawaii 
International Conference on System Science (HICSS-33), 
Waikoloa, Hawaii, 2000. 
[27] M. Maybury, R. D. Amore, and D. House, "Awareness 
of Organisation Expertise," International Journal of Human-
Computer Interaction, vol. 14, pp. 199–217, 2002. 
[28] A. Vivacquan and H. Lieberman, "Agents to Assist in 
Finding Help," presented at ACM Conference on Human 
Factors in Computing Systems (CHI2000), 2000. 
Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
	Select a link below
	Return to Main Menu
	Return to Previous View
