REPLANTING THE ANSWER GARDEN

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

locating.

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.

1 INTRODUCTION

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

literature.

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

technology.

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

373

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

373-378

DOI: 10.5220/0003019703730378

 SciTePress

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

researchers.

2 PERMEABLE BOUNDARIES

OF EXPERTISE

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.

3 SEEDING AND GERMINATING

EXPERTISE

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

others.

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

-Intuition

-Experience in legal setting

-Tacit and difficult to express

Lawyer Expertise

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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.

4 CULTIVATING EXPERTISE

THROUGH DECISION

SUPPORT TECHNOLOGY

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

problem.

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

GDSSs

Human expert

ESs

Human

Technology

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375

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

system.

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

addressed.

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

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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.

5 DISCUSSION AND FUTURE

WORK

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.

6 CONCLUSIONS

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

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377

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.

ACKNOWLEDGEMENTS

This research has been partially supported by the

Catalan Government.

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