Cultivating Expertise through Decision Support Technology
Stephen R. Diasio and Núria Agell
ESADE Business School, Av. Pedralbes 60-62, Barcelona, Spain
Keywords: Expert systems, Collective intelligence, Prediction markets, Group decision support systems, Expertise
Abstract: A growing body of literature established within the information technology field has focused on augmenting
organizational knowledge and expertise. Due to increasing environmental complexity and changing
technology the exogenous assumptions found within must be readdressed. Expert systems, group decision
support systems, and collective intelligence tools are presented to illustrate how expertise needed in
organizational decision-making is changing and may not reside within the traditional organizational
boundaries. This paper suggests future research streams of how expertise can be cultivated through decision
support technologies and how organizational expertise and problem-solving can be augmented reflecting the
changing roles of experts and non-experts.
Over the past several years the information
technology (IT) field has contributed to the
understanding of where the best source of expertise
can be found for problems organizations face. IT
researchers have made arguments for the benefits of
expertise recommendation systems and expertise
locating systems (McDonald and Ackerman, 1998)
such as the Answer Garden. For several conferences
within the IT research field have presented field
study findings from the Answer Garden to help
match organizational problems to solution providers
in the organization (Ackerman, 1994; Ackerman and
McDonald, 1996) and have made contributions to
the understanding of location mechanisms of
expertise. However, the altering organizational
landscape due to increasing environmental
complexity and changing technology has required us
to readdress some of the exogenous assumptions.
Though salient issues critical for future development
and theory of decision support technologies
surfaced, (Ackerman and McDonald, 1996) such as
highlighting the limitations of experts and the need
for future systems to ameliorate social and
behavioural environments, these studies narrow use
of experts and focus on advanced users of
technology make generalizing limited. Furthermore,
investigation into joint support systems that
organizations use that augment knowledge and
expertise can be beneficial. Consequently, by
readdressing these suppositions and shortcomings on
where the best source for organizational problem-
solving and expertise can be found, IT research can
replant and further cultivate expertise through
decision support technology. Thus, additional
investigation relevant to today’s organizational
demands and constraints are needed in the IT
To understand how organizations have managed
expertise through technology, this study focuses on
expertise supported by expert systems (ESs), group
decision support systems (GDSSs), and collective
intelligence tools (CI tools) and provides a
comparative analysis of them. These three decision
support technologies will illustrate and provide
insight into how expertise needed in organizational
decision-making is changing and may not reside
within the traditional organizational boundaries. A
support styles framework for practice is introduced
mapping dimensional styles of decision-support
The paper is organized with an introduction of
the changing paradigm between organizations and
their environment. Then we move to defining
expertise and its components. Next, we discuss three
decision support technologies that organizations use
to cultivate expertise. Then we analyze dimensions
R. Diasio S. and Agell N. (2010).
REPLANTING THE ANSWER GARDEN - Cultivating Expertise through Decision Support Technology.
In Proceedings of the 12th International Conference on Enterprise Information Systems - Artificial Intelligence and Decision Support Systems, pages
DOI: 10.5220/0003019703730378
of decision-making as a practice that combines art,
craft, and science as different styles of decision-
making. Finally, we discuss future work for IT
A new paradigm has emerged that allows IT
researchers another opportunity to aid organizations
in finding the best problem-solver for their given
predicament. This new paradigm allows
organization to tap into a larger pool of resources,
knowledge, information, and expertise that is vastly
superior then any human or organization can apply
or build internally. This new paradigm diverges
from traditional thought where high levels of
expertise are thought to be the best source for
problem-solving, to creating a new approach using a
more permeable boundary of organizations.
Similar sediments are expressed within other
research areas emphasizing that “the boundary
between a firm and its surrounding environment is
more porous’ (Chesbrough, 2003, p.37).
Investigation into this alteration of solution
providing is needed in IT research with its objectives
in mind. Seminal decision theory research (Simon,
1947) has highlighted ‘to secure all the advantages
of expertise in decision-making it is necessary to go
beyond the formal [organizational] structure…
(Simon, 1947, p.189) To help rectify direction
within IT research and provide insight into how
organizations are haltering expertise using support
technology, three decision support technology will
be presented that reflects the changing paradigm of
the role that experts and non-experts are playing.
Before researchers can build support technologies to
assist organizations and decision-makers in finding
or harnessing expertise, a review of what expertise is
and what the components of expertise are is needed.
A peak into the expertise literature offers help in
defining its make up as a multidimensional construct
with expert knowledge as the essential part.
Expert knowledge consists of three principle
components (1) formal knowledge, (2) practical
knowledge, and (3) self-regulative knowledge
(Tynjala, 1999). As a result of the complexity of
expert knowledge, full articulation from experts may
be difficult if not impossible (Spender, 1996).
Figure 1 illustrates the components of expertise
using the example of a lawyer. Formal knowledge is
explicit where learning is the focus of factual
information. For instance, a lawyer would know the
laws and case histories from law school. Practical
knowledge develops in the skill of “knowing-how”
and is the tacit knowledge, where intuition plays a
role making expert knowledge difficult to explicitly
express. Lawyers have practical knowledge through
their extensive experiences from being in a legal
setting which better prepares them to make a legal
argument or judgment. The third component, self-
regulative knowledge consists of the reflective skills
that individuals use to evaluate their own actions.
For self-regulative knowledge, a lawyer would
monitor his argument, presentation, and reasoning
while presenting to the judge or jury.
Researchers within the expertise literature would
agree the scarcity of expertise and difficulty in
representing it makes whoever possesses it
extremely valuable because of its influence on
decision-making. Nonetheless, expertise is thought
of as a highly specialized or domain-specific (Chi et
al., 1988) set of skills that have been honed through
practice for a specific purpose (Jackson, 1999) and
perform consistently more accurate in relation to
Since many decisions are dependent on the
available information at hand when a human expert
cannot be found, decision-making can be
compromised if decision-makers do not have access
to the resources, information, and expertise needed
to make a quality decision (Simon, 1947). Thus it is
understandable that organizations have contributed
large amounts capital and resources to help manage
expertise and have turned to decision support
technologies to fill this gap.
Figure 1: Components of Expertise of a Lawyer.
Formal Knowledge Practical Knowledge
Self-regulative Knowledge
-Reflective skill
-Evaluation of action
-Monitor argument and presentation to jury
-Factual Knowledge
-Learning of explicit information
-In school or cases
-Experience in legal setting
-Tacit and difficult to express
Lawyer Expertise
ICEIS 2010 - 12th International Conference on Enterprise Information Systems
In Table 1 a human to technology relational
approach is presented showing the comparison of the
prevailing human and decision support technology
used in organizations. On a human level,
organizations can turn to a centralized human expert
for decision-making or participants can congregate
for a group meeting to make a decision if no one
individual has complete knowledge of the problem.
Organizations can also collect the availing opinions,
feelings, and needs using a decentralized method of
surveying, polling, or voting. These methods are
used in organizations, however have limitations that
are extensively addressed in decision theory research
(Simon, 1947).
On the technology level, organizations have used
ESs to replicate human experts and knowledge in
narrow domains of decision-making. GDSSs
systems facilitate group meetings by enhancing
communication between its participants and
recently, organizations are turning to capture
distributed knowledge of employees and customers
through CI tools.
Table 1: Human to Technology Relational Approaches.
Considering expertise is not only restricted to
human beings- rather technology’s capacity to
posses “expert” ability to influence decision-making
through the transfer of knowledge, organizations
have allocated significant resources to leverage
expertise using technology. Each technology or
system has been built to better capture knowledge or
represent expertise in the cognitive process of the
decision-maker(s) for effective decision-making to
occur (Liou, and Nunamaker, 1990; Smith, 1994).
Expertise captured and managed from the support
systems embody why each system is important for
organizations to have. In re-examining the
literature, organizations have cultivated different
support technologies to support their expertise needs
when human experts can not be found or no one
person has complete knowledge of the problem.
One method used by organizations to capture
expertise is to employ expert systems. Currently,
expert systems are playing a critical role for many
organizations and are a source of competitive
advantage (Gill, 1995). Expert systems, a branch of
artificial intelligence are contributing to decision-
making through their representation of knowledge
and reasoning of human experts for its users (Weiss
and Kulikowski, 1984). By mimicking and
replicating the cognitive process of a human expert,
novice users can be supported to perform as well as
experts (Cascante et al., 2002) while expert users
can have their expertise further refined. By
emulating an expert’s problem-solving ability,
knowledge and reasoning are transferred to a user
through the use of ESs for faster learning and
decision-making than would occur when developing
these skills over time. Organizations use ESs
because they represent expertise to its users for
decision-making when a human expert cannot be
found or is in short supply.
Although many organizations have successfully
implemented expert systems to address particular
problems in a narrow domain, changing external
factors impacting competitiveness and sustainability
have forced organizations to approached critical
decisions differently. Studies indicate (Gannon,
1977) the more complex organizations become the
fewer decisions are made by any single individual
(or expert system). Rather than rely on expertise
from one individual or system for an important
decision, organizations turn to groups or teams of
experts in the decision-making process.
Furthermore, groups of experts may be necessary
when diverse subsets of knowledge are required and
no single expert has complete knowledge of the
One technology supporting organizational
change and group decision making is group decision
support systems (GDSS). GDSS use has shown to
reduce time, costs (Gallup, 1985), and foster
collaboration, communication, deliberation, and
negotiations (Kull, 1982). Research in group
decision support system theory suggests; that
through the communication, collective knowledge,
and interaction of participant’s better solutions can
be reached over any single individual. When a
GDSS is used in decision-making it aims to improve
the process of group decision-making for opinion
Survey/ Polling
CI tools
Group meeting
Human expert
REPLANTING THE ANSWER GARDEN - Cultivating Expertise through Decision Support Technology
convergence, group consensus, and better outcomes
in decision-making. Designed using the rationale
theory of decision-making, GDSSs optimizes the
decision-making process by following what is
referred to as intelligence, design, and choice
(Simon, 1947). GDSS use enhances decision
outcomes by leveraging the cognitive knowledge of
participants by supporting the behavioural and social
needs of the group to resolve uncertainty in the
group decision making process. GDSSs possess
expertise in the cognitive decision-making process
using techniques developed within the support
Technologies embedded within the GDSS
contribute to the different components of expertise.
For instance, a database or information repository is
one component of GDSS and offers the formal or
documented knowledge of expertise. Practical
knowledge of expertise can be viewed through the
heuristics used in the GDSSs to analyze judgments
or techniques in decision-making. Communication
technologies such as email, instant messaging, and
video conferencing allow for interaction to occur
representing the self-reflective knowledge of
expertise to arrive at a decision.
As a result of the different technologies that
support the components of expert knowledge,
GDSSs are able to capture the knowledge and
contribution from the individual users collaborating
to arrive at a better solution or create a greater sum
than the individual parts. In addition to the cognitive
expertise, GDSSs occupy the center point for the
aggregation of information and expertise from each
participant. GDSSs impact on the decision-process
outcome depends on the degree of change in
communication of the users and when used
effectively better outcomes can occur. Though
GDSSs have failed to build traction as an effective
support system, they are continuing to used and have
adapted to the market’s organizational and
technological needs of the 1990’s by moving
primarily to a web-based software allowing for
anytime, anyplace meeting, and decision-making.
Though GDSSs have supported organizations by
utilizing the expertise of the group and providing
structure for effective decision-making (White et al.,
1980), decision-makers are still constrained by the
information they receive to make a decision. Since
the quality of group discussion is greatly contingent
upon the quality of information brought to the
session by the group members, having tools with
capabilities to increase available information
internally and externally to the organization would
be beneficial (Aiken et al., 1991). In hindsight, what
is alluded to, is a changing organizational paradigm,
away from a half century of support system
development and research that centralized decision-
making for experts, to a decentralized model of
managing external capabilities, resources, and
information of the organization. In organizations,
decision-makers do not have access to all the
information they need when making a decision
(Simon, 1947) and thus, effective decisions can be
compromised. Three potential reasons why critical
information is not accessed by decision-makers
could be: conventional methods and technologies
insulate information flow to only a select group of
people, decision-makers do not ask for all the
information accessible to them, or those who have it
do not share because of political or social reasons.
As Friederich von Hayek (1945) expresses in his
well known article: The Use of Knowledge in
Society, regarding the economic problem of society
“…is to secure the best use of resources known to
any of the members of society, for ends whose
relative importance only these individuals know. Or,
to put it briefly, it is a problem of the utilization of
knowledge not given to anyone in its totality.”
Through the use of collective intelligence tools (CI
tools), constraints and limitations to collect
information and knowledge in its totality can be
Based on the premise that the collective
judgment of a large group is better at predicting and
forecasting future events than individual experts or
small groups of experts (Hanson, 1999; Berg et al.,
2001) collective intelligence offers a substitute to
traditional experts and solution providers. CI tools
that support information aggregation offer an
alternative to the constraints of information flow in
decision-making, knowledge work, and complexity
in forecasting uncertain events. Moreover, the
primary goal of CI tools is to facilitate the
summative body of knowledge, information, and
resources of its users.
Contrasting sharply to traditional decision
support tools, CI tools democratize decision-making
by including many people in and outside the
organization into the information gathering and
decision-making process. Diverging from traditional
thought where high levels of expertise are seen as
the best source of decision-making, CI tools have the
ability to harness lower levels of expertise for peak
solutions in decision-making (Page, 2007).
Prediction markets, a CI tool can be defined as
markets that are designed for the purpose of
collecting and aggregating information that is
scattered among the traders (users) who participate
ICEIS 2010 - 12th International Conference on Enterprise Information Systems
through trading. When a user participates by trading,
information can be reflected in the market values in
order to make predictions about specific future
events (Wolfers and Zitzewitz, 2006). Instead of
independently-derived individual predictions,
predictions markets enable a collaborative
evaluation process where many participants make
small contributions with a granularity effect.
Derived from the efficient markets hypothesis,
markets are expected to be the best predictor of
unknown future events and should be seen as a
complement to executives and experts to aid in
information flows to make decisions more quickly
and accurately. Much like a real market, traders are
rewarded monetarily or through visibility with in the
organization based on the accuracy of the
information they provide by participating. When
individuals buy or sell contracts based on the
information they have, they will be rewarded by
being the first mover to reflect this new information
into the market before others.
Since CI tools are in their formative stages of
development and use, their robustness is yet to be
demonstrated (Diasio and Agell, 2009). For instance,
depending on the type of CI tool organizations
implement they may or may not have a component
of formal knowledge. Currently, prediction markets
do not contain a formal knowledge moiety, however
in the near future it is possible the addition of linked
database, knowledge repositories, or automated
market monitoring software is conceivable. As with
formal knowledge, prediction markets do not
possess practical knowledge but as usage grows
heuristic trading components will facilitate trading
virtually. The market mechanism within prediction
markets act as the self-regulative knowledge that
create this reflection in the market price through
buying and selling.
Seeking to push decision-making down the
corporate ladder and information up toward the top
to those who need it, CI tools incubate the hidden
information that is scattered around the organization
or network to be discovered that allows non-experts
to produce expert like results when collectively
mobilize. By including a large number of people
such as rank and file workers or the public into the
decision-making process, organizations can create
opportunities to augment their expertise needs.
Organizations that effectively mobilize a diverse
group of people and tap a new reservoir for problem-
solving, transform individuals with a low level of
expertise for a given problem into an additional
method for forecasting, decision-making, and
problem-solving. Companies that choose to use CI
tools leverage resources of knowledge, information,
and problem-solving ability far beyond what they
could afford to deploy internally. CI tools help link
and manage the external information, knowledge,
and expertise of the organization and enable
organizations to apply outside knowledge and
expertise towards improving decision-making and
problem-solving in the organization.
Today the internet has made it easier and more cost
effective for organizations to implement CI tools to
guide information flow. However, it is organizations
choice and ability to effectively manage and
leverage the collective intelligence of its resources.
Uses of CI tools by organizations have had some
success (Ho and Chen, 2007) however; much is still
unknown about these tools. Future challenges may
include using CI tools not as a replacement for
experts but as an additional tool in decision-making.
Traditional roles of experts may change and
represent a mindset shift from answer givers to
inquiry mediators in effort to harness the knowledge
of the masses in decision-making.
The limitations of existing expertise locating
methods have forced organizations to rethink where
knowledge and expertise can be found. As the use of
CI tools grow, opportunities exist to apply far
greater knowledge resources to a wide spectrum of
problems then any individual or group of expert
could employ. CI tools support hard to find
information that would not be included in problem-
solving, create an efficient method for aggregating
large amounts of information, and incorporates new
and diverse perspectives giving organizations a
greater opportunity to find solution providers.
Our review has shown how organizations use
decision support technology to support their
expertise needs in decision-making. The study has
indicated a shift from the existing IT literature that
reflects a changing paradigm where organizations
can find and leverage expertise. As a result of the
permeable boundaries of the organization, new
technologies that bridge the external environment to
organizations are emerging. These changes have
significant impacts on organizations, where experts
REPLANTING THE ANSWER GARDEN - Cultivating Expertise through Decision Support Technology
and non-experts may find themselves playing new
roles within the organizational structure.
Companies who take the necessary steps to
integrate these technologies which utilize legacy
support systems and emerging decision support
technologies will be rewarded with a competitive
advantage through accuracy in forecasting and
problems-solving with more robust support systems.
Finally, this paper lays a foundation for a
research stream in understanding the role decision
support technologies play supporting expertise in
organizations by: (i) showing a perspective of
expertise supported by decision support technologies
that organizations currently use and (ii) in
structuring how organizational expertise in short
supply can be augmented using CI tools from
outside the organization.
This research has been partially supported by the
Catalan Government.
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