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Journal of Management
XX(X) 1 –31
© 2009 Southern Management
Association
DOI: 10.1177/0149206309350084
http://jom.sagepub.com
Expertise-Based Intuition and
Decision Making in Organizations
Eduardo Salas,1 Michael A. Rosen,1 and
Deborah DiazGranados1
Abstract
There has been a growing popular fascination with how experts make rapid and effective
decisions. This interest has been paralleled in various scientific research communities. Across
these disciplinary boundaries, researchers have found that intuition plays a critical role in
expert decision making. Therefore, an understanding of how experts develop and use intuition
effectively within organizations has the potential to greatly influence organizational practices
and effectiveness. The purpose of this review is to integrate the extant literature related to
expertise-based intuition—intuition rooted in extensive experience within a specific domain—in
decision making. To that end, this review addresses four specific goals. First, the authors review
the scientific literature on expertise and intuition to define expertise-based intuition, the type
of intuition of most value to organizations. Second, the authors propose a set of descriptive
developmental and performance mechanisms of expertise-based intuition in decision making.
Third, the authors discuss the multilevel nature of expertise-based intuition. Fourth, the authors
propose future directions for research and application.
Keywords
expertise, decision making, intuition, judgment
Introduction
In management and more broadly in all Western scientific disciplines investigating decision
making, there has been a tight focus on explicit deliberation, what Haidt (2001) called the
“worship of reason.” This is understandable in that we consciously experience deliberative
reasoning and hence find it readily accessible for introspection and systematic study. How-
ever, it is increasingly apparent that deliberative reasoning is but one part of a much more
sophisticated cognitive system (Evans, 2008). Conscious deliberation and reasoning are the
“tip of the iceberg” in terms of how people make decisions, and it frequently is not the primary
driver of behavior (Loewenstein, 1996; Reber, 1992). Fast and affect-rich intuitions play a
large role in the decisions people make. In addition, expert performance in many fields relies
1University of Central Florida, Orlando, FL, USA
Corresponding Author:
Eduardo Salas, 3100 Technology Parkway, Orlando, FL 32826, USA
Email: esalas@ist.ucf.edu
Journal of Management OnlineFirst, published on October 28, 2009 as doi:10.1177/0149206309350084
2 Journal of Management XX(X)
on domain-specific intuition developed through extensive practice and experience (e.g.,
Abernathy & Hamm, 1995; Klein, 2003). Several general audience books summarizing the extant
anecdotal and scientific work on intuition (e.g., D. G. Myers, 2002; Gladwell, 2005; Klein, 2003)
have created a flurry of interest in the topic, and recent reviews within management and related
literatures have provided evidence that intuition is a valid construct within the organizational sci-
ences and indeed critical to effective decision making in many settings (e.g., Dane & Pratt, 2007;
Hodgkinson, Sadler-Smith, Burke, Claxton, & Sparrow, 2009; Hodgkinson, Langan-Fox, &
Sadler-Smith, 2008; Sadler-Smith & Sparrow, 2008). However, intuition is not a panacea. Rely-
ing or overrelying on intuitions in certain circumstances can be a source of error.
Consequently, it is important to understand the conditions under which intuition is likely to
be accurate and lead to good decision-making outcomes and when it is likely to lead a decision
maker astray. To this end, the present review expands on the current literature by focusing on
expertise-based intuition in organizations. Expertise is at the root of effective intuitive deci-
sion making in complex organizational settings, and therefore understanding how to develop
and manage effective intuition in organizations is, in part, linked to an understanding of human
expertise. This review addresses two core issues arising from this expertise-based intuition per-
spective. First, for organizations to better support expert decision making, it is important to
understand how experts use intuition in decision-making processes. Decisions need not be based
purely in intuition or purely in deliberation. Frequently, experts use a mixture of strategies.
Second, for organizations to better cultivate expert decision makers, it is important to understand
how experts develop their intuitive capacity. To that end, the present review addresses four
specific goals. First, we draw on both the science of intuition and expertise to clearly define
expertise-based intuition in decision making, how it differs from other types of intuition, and
its value to organizations. Second, we synthesize the large qualitative and descriptive literature
on expertise-based intuition and decision making in field settings conducted within the natural-
istic decision making (NDM) tradition with theoretical and empirical literature from the
management, organizational, and related sciences. The purpose of this integration is to define a
set of performance and developmental mechanisms of expertise-based intuition in decision
making. The proposed mechanisms serve as an outline of how intuitive expert decision making
works and how it is developed. Third, we address the nature of expertise-based intuition in team
settings. Decision making in organizations frequently occurs within teams and expertise-based
intuition plays a major role at the team level as well. Fourth, we discuss implications of this
review for future research.
Expertise-Based Intuition
Intuition has been a topic of research in a variety of scientific disciplines. This breadth of
perspectives has produced surprising amounts of convergence on some points and perhaps
less surprisingly, disagreement on others. In this section, we provide an overview of the sci-
ence of intuition with the aim of clearly defining expertise-based intuition. Several recent and
comprehensive reviews have addressed general issues of conceptual clarification and synthe-
sis across disciplines (e.g., Dane & Pratt, 2007, 2009; Hodgkinson et al., 2008). Consequently,
in this review we focus on connecting the general literature on intuition with the science of
expertise. Subsequently, we discuss the factors contributing to the use and effectiveness of
intuition in decision making. The expertise of the decision maker is a primary factor, but
other characteristics of the decision maker, the decision task, and the decision environment
contribute as well.
Salas et al. 3
Defining intuition
One of the major roadblocks to developing a science of intuition has been a lack of definitional
clarity. A definition that solidly grounds intuition as a legitimate construct for scientific inquiry
has been of critical need, one magnified by the somewhat mystical connotation of intuition in
the general public. As detailed more fully in the following sections, intuition has been described
in terms of expertise (L. A. Burke & Miller, 1999), heuristics (Gigerenzer, 2007; Tversky &
Kahneman, 1981), implicit learning and memory (Lowenstein, 2000), and individual differences
in processing styles or decision-making modes (Epstein, Pacini, Denes-Raj, & Heier, 1996;
Hammond, 1996) as well as lower level perceptual processing (Volz & von Cramon, 2006).
Given the breadth of conceptual and methodological approaches taken to study intuition, the
definitional diversity in the literature is not surprising. Recently, an apparent consensus on some
of the key definitional issues has begun to emerge as well as the essence of intuition across the
various scientific perspectives. Most fundamentally, distinctions between the inputs, processes,
and outcomes of the intuitive thinking process are being solidified. Betsch (2008b: 4) provided
a descriptive definition of these three core components of intuition:
Intuition is a process of thinking. The input to this process is mostly provided by knowl-
edge stored in long-term memory that has been primarily acquired via associative learning.
The input is processed automatically and without conscious awareness. The output of the
process is a feeling that can serve as a basis for judgments and decisions.
Intuition therefore can be thought of as a type of cognition that is qualitatively different than
conscious and analytical reasoning. As will be described in the following, there is strong
evidence supporting the notion that there are two distinct information processing systems in
the human brain, one conscious and deliberative and the other unconscious and intuitive.
Intuition is rooted in this unconscious information processing system, as are a host of related
phenomenon such as implicit attitudes and goals (Hassin, Uleman, & Bargh, 2005). The
outcome of this intuitive processing is the phenomenological experience of an intuition, the
experience of knowing without knowing the reasons why. Dane and Pratt (2007) offered a
definition that clearly articulates the nature of the output of intuitive processing, an intuition.
Specifically, intuitions are “affectively charged judgments that arise through rapid, nonconscious,
and holistic associations” (Dane & Pratt, 2007: 40). These intuitions can be viewed as “quick
appraisal[s] based on integrating information in a sketchy way” (Segalowitz, 2007: 144).
One of the central premises of this review is that these quick appraisals emanating from the
intuitive information processing system are a fundamental component of expert decision making.
This point is well supported by theory and indicates the central role of automaticity (i.e., rule-
based performance practiced to the point where it can be performed without conscious effort;
Moors & De Houwer, 2006) and associative memory to effective intuition (Dane & Pratt, 2007;
Sadler-Smith & Shefy, 2004; Weber & Lindeman, 2008). This however begs the question of how
to differentiate expertise-based intuition from the general concept of intuition.
Expertise-based intuition can be defined using developmental models of intuition and exper-
tise. Baylor (2001) proposed that the development of intuition follows a U-shaped curve with
the x-axis representing “level of expertise” and the y-axis representing “availability of intu-
ition.” Early use of intuition is characterized as “immature” intuition and precedes general
rule-based performance. It is not based on extensive domain-specific knowledge. As
4 Journal of Management XX(X)
the decision maker develops abstract rule-based knowledge of a domain, the availability of
intuition decreases (i.e., the nadir of Baylor’s U-shaped curve). However, in the later stages of
experience, intuitions again become prevalent due to the decision maker’s accumulated experi-
ence. This type of intuition is qualitatively different than the previously discussed “immature”
or novice intuition because it draws on domain-specific knowledge. This type of intuition has
also been referred to as educated intuition (Hogarth, 2001). Therefore, we can define expertise-
based intuition as the intuitions occurring at these later stages of development where the
decision maker has developed a deep and rich knowledge base from extensive experience
within a domain. The Venn diagram presented in Figure 1 illustrates the components of intu-
ition and expertise, which are unique and overlapping.
Dual Processing Theories
Across a variety of disciplines seeking to understand human cognition, a general framework
describing human information processing in terms of two distinct systems has emerged (see
Chaiken & Trope, 1999; Evans, 2008, 2009; Moskowitz, Skurnik, & Galinsky, 1999). These
dual processing systems come in a variety of forms and describe a wide variety of phenomena
(e.g., learning, attitudes, decision making, moral judgments, etc.). Unfortunately, there are
almost as many labels for each of these systems as there are dual processing theories (e.g.,
automatic and controlled, Schneider & Schiffrin, 1977; experiential and rational, Epstein,
1994; holistic and analytic, Nisbett, Peng, Choi, & Norenzayan, 2001; reflexive and reflec-
tive or X and C systems, Lieberman, Jarcho, & Satpute, 2004; associative and rule-based,
Sloman, 1996; conscious and unconscious, Dijksterhuis & Nordgren, 2006; intuitive and
Figure 1. Venn diagram depicting the overlap and distinction between the constructs of intuition and
expertise
Salas et al. 5
analytic, Hammond, 1996). Although there are important distinctions between these models,
they are thematically related in that they describe one system that is fast, holistic, and does not
require conscious cognitive effort (i.e., the intuitive system, or System 1) and a second system
that is slower, analytic, and cognitively effortful (i.e., the conscious deliberative system, or
System 2). For the purposes of this review, we adopt the terminology of Stanovich (1999) and
others (see Evans, 2008) and refer to System 1 as a general label for the rapid unconscious
information processing system and System 2 as the slower, conscious system.
Evans (2008) provided an extensive review and analysis of dual processing theories of cog-
nition and identified four clusters of attributes commonly ascribed to these two systems. The
first is consciousness. Cognitive processing in System 2, the deliberative system, is consciously
accessible whereas it is largely unconscious in System 1. Second, the two systems are thought
to differ in their evolutionary development, with System 1 being the older, more primitive
system and System 2 being more recent. Third, these systems differ in terms of their functions.
System 1 functions in a domain-specific and contextualized manner using associative parallel
processing. System 2, however, functions in an abstract, sequential, and rule-based manner.
Fourth, Systems 1 and 2 differ in terms of individual differences, with System 1 exhibiting very
little between-person variation as it is independent of working memory and general intelligence
and System 2 varying more widely between individuals in terms of capacity and ability.
Lieberman (2000: 110) provided a framework for understanding the neural, cognitive, and
social aspects of intuition rooted in dual processing distinctions. His central premise is that
“intuition is a phenomenological and behavioral correlate of knowledge obtained through
implicit learning.” Implicit learning is a System 1 mechanism whereby information is acquired
without directly attending to it and largely without conscious awareness that the information
has been learned (Reber, 1992).
Much of the early work from dual processing perspectives has set about to detail the proper-
ties of each system; however, a major challenge for future research is to better understand how
these systems work together (Gilbert, 1999; Gray, 2004). A decision is rarely either intuitive or
deliberative because both systems are functioning in parallel and interacting in complex ways
(Hammond, Hamm, Grassia, & Pearson, 1987). Deliberate thinking can serve two purposes:
(a) evaluate the product of intuitive processing (e.g., a decision maker may use reason to over-
ride an initial intuition, although research suggests this is relatively infrequent, especially when
intuition is accompanied by intense affect) and (b) uncover new information that is acted on by
the intuitive system (e.g., people engaging in deliberative perspective taking will frequently
have an immediate and visceral response of guilt or empathy to the plight of another person).
The general tendency in dual process theories is to frame this interaction in terms of System 1
subservience to System 2. That is, intuitions serve as inputs to deliberative processes, but the
deliberative system is the focus, the “executive” function that has the final say in action selec-
tion. If this is the case, intuition plays a major role in guiding deliberative decision making.
However, a different perspective on the relationship between these systems exists and elevates
the role of intuition even further. Haidt (2001; Haidt, Patrick Seder, & Kesebir, 2008) pro-
posed an intuition-based model of moral judgment that makes distinctions between System 1
and System 2 processes. In this model, moral judgments are made primarily through System 1
processing. The role of System 2 analytical reasoning primarily is to generate post hoc
rationalizations for why a specific judgment was made, but these rationalizations rarely result
in a change in the initial judgment.
6 Journal of Management XX(X)
In sum, there is mounting theoretical and empirical evidence that the human brain is able
to quickly and effectively capitalize on past experience using the rapid and unconscious pro-
cessing of System 1. Dual processing theories provide a means for removing intuition from an
esoteric or mystical domain into the mainstream of science. However, to truly understand how
intuition is used effectively in organizations, it is necessary to understand the expertise that
underlies accurate intuition. In the following section, we provide an overview of the science of
expertise.
Defining Expertise
Expertise in a general sense is high levels of skill or knowledge within a given domain. The
origin and nature of expertise have received much attention from researchers (for a review of
the history of expertise studies, see Ericsson, 2006). The most recent developments of this rich
tradition can be grouped into two stages (Holyoak, 1991). In an early stage of conceptualiza-
tion, expertise was viewed as a skill in applying a limited number of reasoning strategies and
heuristic searches such as means-ends analysis and hill climbing (Hayes, 1989; Newell &
Simon, 1972). People were assumed to explicitly and consciously attend to all of the critical
information in a problem space and apply rules or propositions to move them closer to a goal.
From a dual processing perspective, this problem-solving approach to expertise relies entirely
on deliberative processing to explain performance. The rules and search strategies that suppos-
edly described expert performance were domain independent.
Empirical studies from a variety of domains refuted the idea that expert performance was
achieved via application of context-free rules. Expert performance was found to be domain
specific, requiring specialized knowledge (Chase & Simon, 1973; de Groot, 1978), and it was
instead the novice’s performance that was best characterized as the application of general rea-
soning strategies (Dreyfus & Dreyfus, 1986). These findings gave rise to the knowledge-based
view of expertise; that is, experts achieve high levels of performance primarily through domain-
specific knowledge and other performance mechanisms acquired through prolonged periods of
experience and focused practice (Ericsson, Krampe, & Tesch-Romer, 1993).
This general domain-specific view of expertise includes such mechanisms as the amount
and structure or organization of knowledge (Chi, Glaser, & Rees, 1982; Larkin, McDermott,
Simon, & Simon, 1980), context-dependent and specialized reasoning strategies (Dorner &
Scholkopf, 1991; Schunn, McGregor, & Saner, 2005), an adaptive set of heuristics (Gigerenzer,
Todd, & the ABC Research Group, 1999), and specialized memory skills (Ericsson & Kintsch,
1995), among other factors (see Ericsson, Charness, Feltovich, & Hoffman, 2006). The
domain-specific nature of expertise has made it difficult to develop a simple set of generaliz-
able descriptors of expert performance mechanisms. Instead, expertise is viewed as adaptation
to the task constraints (Ericsson & Lehman, 1996), and the broad array of performance mecha-
nisms identified in the literature are viewed as a “prototype” of expertise (Hoffman, Feltovich,
& Ford, 1997; Sternberg, 1997). The specific mechanisms underlying expertise in any given
domain vary as a function of the nature of the task.
While expertise in the form of complex domain-specific schemas (Dane & Pratt, 2007)
underlies expertise-based intuition, expertise comprises much more than just intuition. Expert
decision makers use a combination of deliberative and intuitive strategies. Therefore, expertise
and intuition are by no means synonymous. In the following section we discuss factors that influ-
ence the tendency for decision makers to rely on intuition as well as the effectiveness of intuitions.
In later sections we discuss the mechanisms of expertise-based intuition in more detail.
Salas et al. 7
Core Factors Influencing Intuition
There is an ongoing debate over whether or not intuition is always accurate (for a detailed review
of this debate, see Bastick, 2003, chapter 8), but the preponderance of evidence suggests that
under certain circumstances intuition is highly accurate. However, it does not imbue a decision
maker with omniscience and therefore has limits and produces errors. Understanding when intu-
ition is likely to be accurate or inaccurate is especially important in situations where deliberative
decision-making and intuitive decision-making outcomes diverge (Plessner & Czenna, 2008).
That is, if both deliberation and intuition lead to the same ends, it does not matter which is relied
on. But, when a person’s experience suggests a different option than the application of some
context-free rule, then the decision maker’s consequences of errors in either system become real.
The literature suggests that there are several conditions under which intuition is more likely to
be accurate. Characteristics of the decision maker, the decision task, and the decision environ-
ment have all been shown to influence both the tendency to use intuition as a basis for decisions
as well as the accuracy of those intuitions. Key factors in each of the aforementioned categories
are summarized in Table 1 and discussed in the following.
Decision Maker. Two features of the decision maker that have been linked to the effectiveness
of decision making are expertise and individual differences in processing styles. These two
features are expanded on in the following sections.
Expertise. As previously discussed, intuition is most likely to be effective when the decision
maker is knowledgeable and experienced within a domain (Hogarth, 2001). Intuition is based
in implicit learning (Lieberman, 2000) and automaticity (Moors & De Houwer, 2006), and the
more experience this learning system has processed the better it will be at detecting important
patterns in the environment. A decision maker is most likely to benefit from the use of intuition
when his or her implicit knowledge adds above and beyond what explicit and rule-based learn-
ing can account for (Plessner & Czenna, 2008). In the following sections, the nature of expertise
in intuitive decision making will be reviewed in detail.
Individual differences in processing styles. From the dual processing perspective outlined previ-
ously, there are (at least) two modes or general strategies (i.e., qualitatively different approaches
to making decisions) that decision makers can adopt: intuitive and deliberative (for other taxono-
mies of decision-making modes or styles, see Ames, Flynn, & Weber, 2004; Hammond, 1996).
In most cases, these systems and strategies interact continually, but the degree to which a person
has a tendency to rely on either of the two cognitive systems has been investigated as an indi-
vidual differences variable (e.g., Betsch, 2008a; Stanovich & West, 2000). People have different
preferences for the processing mode, with some tending to engage in more intuitive and affect-
based decision making while others prefer more analytical and deliberative methods.
Various dimensions and scales have been developed and applied to investigate this individ-
ual difference, including the sensing-analytic dimension of the Myers-Brigg Type Indicator®
(I. Myers & McCaulley, 1986), the Rational-Experiential Inventory (REI; Epstein et al., 1996,
1999), the Preference for Intuition and Deliberation Scale (PID; Betsch, 2008a), and the Cogni-
tive Style Index (CSI; Allinson & Hayes, 1996). These scales all capture different aspects of
reliance on affect or cognition to make decisions. Examinations of the Need for Cognition and
Faith in Intuition subscales of the REI have shown good construct validity (Epstein et al., 1996)
and consistency with dual processing views of intuition (Hodgkinson, Sadler-Smith, Sinclair,
& Ashkanasy, 2009). In addition, the Preference for Intuition and Preference for Deliberation
8 Journal of Management XX(X)
scales of the PID moderated the effect of implicit and explicit attitudes on decisions; for
people with a preference for intuition, implicit attitudes predicted decisions, and for people
with a preference for deliberation, explicit attitudes predicted decisions (Betsch, 2004). These
differences in the tendency to rely on intuitive processing are thought to be preferences and
not differences in intuitive capacity or ability, as intuition is based in implicit learning and
there are no substantial differences between people in this system (Reber, Walkenfeld, &
Hernstadt, 1991). For more detailed reviews of intuitive styles or strategies, see Betsch (2008a)
and Hodgkinson et al. (2008).
Decision Task. Two general features of a task have been linked to the effectiveness of decision
making as well as the propensity for a decision maker to adopt an intuitive decision-making
style: task structure or type and the availability of feedback.
Task structure. Hammond (1996; Hammond et al., 1987) proposed a task continuum where a
variety of task properties will influence a decision maker’s propensity to use intuition as a basis
for a decision (see also Dunwoody, Haarbauer, Mahan, Marino, & Tang, 2000). Task features
inducing intuitive processing include large sets of redundant cues presented simultaneously
where there is no organizing principle. In a similar vein, Dane and Pratt (2007) proposed that
the effectiveness of intuition will differ based on whether or not the decision-making task is
intellective or judgmental in nature (Laughlin & Adamopoulos, 1980). Specifically, intuition
will be more effective in judgmental tasks.
Table 1. Primary Factors Influencing the Use and Effectiveness of Intuition in Decision Making for
Individuals
Decision maker
Decision task
Decision
environment
Factors
influencing
use and
effectiveness of
intuition
Expertise
Processing
styles
Task structure
Availability of
feedback
Time pressure
Description
Extensive experience within a domain
can produce automaticity and a large
and well-organized knowledge base,
affording intuitive pattern recognition
capacities
People are predisposed to rely more on
either intuition or deliberation
Intuition is more likely to be effective
in judgmental tasks with large sets of
cues to integrate
Both implicit and explicit memory
development is facilitated by feedback
Increasing levels of time pressure are
associated with more reliance on
intuition as deliberative processing
is a more time consuming mode of
cognition
Example citation
Klein (1993, 2003);
Dane and Pratt
(2007)
Stanovich and West
(2000)
Hammond (1996);
Khatri and Ng
(2000); Dane and
Pratt (2007)
Hogarth (2001);
Ericsson, Krampe,
and Tesch-Romer
(1993)
Lipshitz, Klein, Orasanu,
and Salas (2001)
Salas et al. 9
In general, intuition is most likely to be effective when the situation is complex. Conscious
deliberation is a “low capacity” channel and can quickly be overwhelmed by large amounts of
information; however, intuitive processing is parallel in nature and quickly integrates complex
sets of cues. If a task is simple enough, consciously applying a logical rule is likely to be more
effective than the use of intuition. Issues of task complexity involve the task type (e.g., tasks with
many solutions varying in degrees of acceptableness favor intuition and those with a clear crite-
rion for success favor deliberation) and environmental uncertainty (Dane & Pratt, 2007).
Khatri and Ng (2000) examined the intuitive process as observed in strategic decision
making by surveying senior managers of companies representing computer, banking, and util-
ity industries in the United States. They found that intuitive processes are in fact used in
organizational decision making. Their findings indicated that those managers in the computer
industry utilized intuition to a much greater extent than those in the banking and utilities indus-
tries. Furthermore, their analysis of the intuition and performance relationship found that the
use of intuitive synthesis was positively associated with organizational performance in an
unstable industry but negatively in a stable industry.
Availability of feedback. Intuition is most likely to be effective when feedback is available. All
experience is not created equal when it comes to the development of intuition. It is necessary to
develop implicit memories that clearly map features of the environment. With the roots of intu-
ition firmly planted in implicit (or associative) memory, the conditions that facilitate implicit
learning also facilitate the development of effective intuition (Hogarth, 2001). In addition, the
compilation of explicitly learned knowledge and the development of automaticity involve
focused practice in the presence of feedback (Dreyfus & Dreyfus, 1986; Ericsson et al., 1993).
The use of feedback in the development of expertise-based intuition will be discussed further
in a later section.
Decision Environment. In addition to characteristics of the decision maker and the decision
task, the environment surrounding the task has been identified as important to the use and effec-
tiveness of intuition, particularly the presence or absence of stressors (see Hammond, 2000).
Time pressure is one such stressor, with a strong influence on the tendency to use intuition as a
basis for decision making. Specifically, time pressure increases reliance on intuition primarily
because decision makers simply do not have the time to engage in exhaustive search strategies
underlying purely rational models of decision making (Lipshitz, Klein, Orasanu, & Salas, 2001).
Summary. Intuition is rooted in a largely unconscious information processing system, which
produces a rapid and holistic judgment based on complex patterns of temporal and conceptual
relationships. These judgments can further be characterized as knowing (or deciding) without
knowledge of the process by which that decision was made. These judgments are accompanied
by affect that is used in the decision-making process. Dual processing perspectives on human
cognition play an important role in describing how experts make decisions. System 1 (intuitive
processing) affords the decision maker rapid access to large amounts of experience; however,
this is useful only if the present situation the decision maker faces closely parallels those expe-
rienced in the past. If the decision maker is taken out of their expert context (e.g., Hoffman,
2007), then the likelihood of his or her intuition being useful decreases. Consequently, experts
use deliberative processing to evaluate how their past experiences can be applied in the present
context. In the following section, issues of accuracy in intuition are further developed. In the
following sections, we provide more detailed descriptive mechanisms of how experts use and
develop their intuition.
10 Journal of Management XX(X)
Mechanisms of Expertise-Based Intuition
Expertise and intuition are not synonymous; rather, intuition is rooted in expertise. There are
mechanisms of expert decision-making performance that involve intuitive processing and those
that involve deliberate processing. Experts possess the extensive experience and knowledge
necessary to take advantage of intuitive processing, but this alone is not enough. In this section,
we provide a review of two related literatures—the expertise and naturalistic decision making
traditions—that together help explain the role of intuition in expert decision making. NDM is a
relatively new tradition in decision-making research. A core aim of this tradition is to under-
stand “the way people use their experience to make decisions in field settings” (Zsambok,
1997: 4). As a field, it emphasizes the role of the decision makers’ expertise (i.e., what are the
mechanisms of expert decision making in a given domain; Salas & Klein, 2001), as well as
research methodologies that include the rich contextual detail of real-world settings. In essence,
the unit of analysis for NDM researchers involves both the expert and the context in which the
expert performs (Feltovich, Ford, & Hoffman, 1997). This section provides a synthesis of
NDM, expertise, and related findings into descriptive mechanisms of performance and devel-
opment of expertise-based intuition. Both the mechanisms of performance and the mechanisms
of development are summarized in Tables 2 and 3, respectively.
Mechanisms of Performance
The following section provides a set of performance mechanisms that characterize expert intui-
tive decision making. In addition to the discussion provided next regarding the mechanisms of
performance for expertise-based intuition, we refer the reader to Table 4 where we have pro-
vided some highly cited business examples and have extrapolated from the example the
mechanisms of performance utilized in the decision making of the individuals involved.
Large and Well-Organized Knowledge Base. From the naturalistic decision making litera-
ture we know that proficient decision makers are those individuals who have relevant experience
or knowledge in the specific domain. The simple point that experts know more than nonexperts
seems self-evident, but there are important nuances. An expert’s knowledge base is beyond
knowing what is called declarative knowledge (i.e., facts). Experts organize their knowledge in
a more conceptual way (Bordage & Zacks, 1984; Chi & Ohlsson, 2005), with more intercon-
nections between concepts (Feltovich, Johnson, Moller, & Swanson, 1984). They are able to do
this because of the overwhelming amount of knowledge they have gained.
All human beings organize their knowledge in some fashion. Markman (1999) examined the
manner in which individuals organize their knowledge via semantic networks, theories, and
schemas. Semantic networks represent a person’s declarative knowledge organized in a manner
where concepts (nodes) are connected by relations (links). An expert’s semantic network is well
organized and larger in scope than that of a novice. Therefore, it will take less time for an expert
to arrive at a specific determination of a context than it would a novice. This is due to the fact
that an expert will already have connections created that will allow him or her to traverse from
one relation to another when making decisions in a specific situation.
Theories can also represent knowledge of a domain. That is, a theory is organized around a
small set of core concepts, which the other elements of knowledge are dependent on in that
domain (Chi & Ohlsson, 2005). For example, a financial manager based on his or her experi-
ence may have a specific theory regarding the ebb and flow of a stock market in a particular
economy. Moreover, theories are a deep representation of knowledge. Novices differ in their
Salas et al. 11
Table 2. Mechanisms of Performance for Expertise-Based Intuition, With Empirical Support
Mechanisms of
performance
Large and well-
developed
knowledge
base
Pattern
recognition
Sensemaking
Situation
assessment
and problem
representation
Automaticity
Mental
simulation
Description
Expertise-based
intuition uses relevant
knowledge and
experience in the
specific domain. This
knowledge goes beyond
declarative knowledge.
Conceptual and
procedural knowledge
are aspects of this
knowledge base.
Expertise-based intuition
uses a collection of
complex patterns in
a person’s domain to
perceive larger and more
meaningful patterns in
the environment more
rapidly.
The effort exerted to
understand events in
order to create order
and make sense of what
has occurred, what is
occurring, and what will
occur.
Expertise-based intuition
utilizes situation
assessment and problem
representation, which
includes maintaining an
understanding of the
entire picture.
The process by which an
individual can accomplish
a task without using all
cognitive resources.
Provides an evaluation of
a course of action to
a situation, specifically
if the course of action
“fits” the situation.
Key points
• Experts organize knowledge in a
conceptual way.
• Experts organize knowledge
with more interconnections
between concepts.
• Knowledge is organized via
semantic networks, theories,
and schemas.
• Experts view cues as chunks or
patterns.
• Experts’ use of pattern
recognition allows them to
assess environment more
rapidly than novices.
• Affords the ability to use
pattern matching effectively.
• Experts engage in problem
detection, identification,
anticipatory thinking, forming
of explanations, identifying
explanations, discovering
inadequacies in initial
explanations, and projecting the
future.
• Quick judgments can be made
of the situation (e.g., atypical or
familiar).
• Identification and clarification of
the state of a problem.
• Accomplishing a task is
not affected by or affects a
concurrent task.
• Contributes to an expert’s
ability to understand the larger
meaning of a set of events.
• Conscious and deliberate
process.
• Affords the ability to engage in
simulated implementation of the
solution.
• During this process the decision
maker evaluates the quality of
the solution.
Example citation
Bordage and Zacks
(1984); Chi and
Ohlsson (2005);
Feltovich, Johnson,
Moller, and
Swanson, (1984);
Markman (1999)
Biggs and Wild
(1985); Eggleton
(1982); Gobet
and Simon (1996);
Neisser (1976);
Simon and Chase
(1973)
Klein (1993); Klein,
Phillips, Rall, and
Peluso (2007);
Weick (1993,
1995)
Endsley (1995);
Randel, Pugh,
and Reed (1996);
Mosier (1991);
Flin, Stewart, and
Slaven (1996)
Shiffrin and
Schneider (1997)
Klein (2008);
Rutherford and
Wilson (1989);
Klein and Crandall
(1995)
12 Journal of Management XX(X)
knowledge representations in that they are irregular collections of fragments (diSessa, 1988,
1993; Smith, diSessa, & Roschelle, 1993) and not organized in a manner where some things are
more important than others (a key notion of theories is that it represents knowledge).
Schemas are another manner in which knowledge can be organized. Unlike semantic net-
works where it is believed that pieces of knowledge are connected to everything, schemas
represent patterns. When information is retrieved, it is retrieved as a pattern and not based on
connections. These patterns are developed via experiences; therefore, they can change and
grow as new information is acquired.
McCall and Kaplan (1985) wrote that managers are information workers who spend their time
absorbing, processing, and disseminating information about issues, opportunities, and problems.
Table 3. Mechanisms to Develop Expertise-Based Intuition, With Empirical Support
Mechanism of
development
Deliberate
and guided
practice
Self-regulation
Feedback
seeking
Motivation
Goal setting
Example citation
Ericsson (2004); Ericsson,
Krampe, and Tesch-Romer
(1993); Krampe and Ericsson
(1996); Sonnentag and Kleine
(2000)
Bandura (1986); Cohen,
Freeman, and Thompson
(1998); Glaser and Chi (1988)
Shanteau (1987, 1992);
Sonnentag (2000); Cornford
and Athanasou (1995)
Cleary and Zimmerman (2001);
Glaser (1996); Sternberg
(1998a); Winner (1996)
Locke and Latham (1990); Locke,
Shaw, Saari, and Latham (1981);
Mitchell (1997); Seijts, Latham,
Tasa, and Latham (2004)
Key points
• There are four conditions of deliberate
practice:
Repetition of the same or similar tasks
Presence of immediate feedback that can
guide performance improvements
Task builds on the learner’s preexisting
knowledge
Learner who is motivated to engage in
practice and performance improvement
activities
• Deliberative process
• Involves conscious monitoring and self-
assessment of performance processes
• Three types of self-regulation:
Of the environment
Of internal cognitive and affective states
and processes
Of behavioral performance processes
• Feedback must be available to develop
expertise
• Experts seek out feedback
• Feedback is essential for effective learning
• Experts have a “rage to master”
• Is the key driver of expertise development
• Internal need for learning and performance
improvement
• Provides a decision maker with focus
• Develops their task strategies
• Use performance goals to focus on attaining
the distal goal and learning goals to help with
developing knowledge
Salas et al. 13
Managers must manage an extremely complex amount of information (Mason & Mitroff, 1981;
Starbuck & Milliken, 1988). Walsh (1995) argued that managers use knowledge structures to
represent their information and facilitate information processing and decision making. Research
on work experience conducted by Day and Lord (1992) found that experts, as represented by
CEOs, were able to categorize ill-structured problems much more quickly than novices, as repre-
sented by MBA students. Day and Lord argued that it was because of the expert’s well-developed
knowledge structures that allowed them to perform quicker. Similarly, researchers have found that
the number of schema categories vary with experience. Specifically, results have indicated that
novices have more schema categories but the categories contain fewer informational units
(Lurigio & Carroll, 1985; Rentsch, Heffner, & Duffy, 1994; Sujan, Sujan, & Bettman, 1988).
Table 4. Business Examples of the Use of Expertise-Based Intuition
Business example
Apple:
Development of
the computer
Apple I
Honda: Entering
the U.S.
motorcycle
market
Citizens Federal
Bank: Change in
business strategy
to a mortgage
bank
Johnson &
Johnson: Pulling
Tylenol from
the shelves
after cyanide
poisoning scare
Sony: The creation
of the Walkman
in 1979
Key points
Steve Jobs provided the
knowledge that he had gained
from his experience in the
industry to develop the PC
board.
Two scouts appointed by
Takeo Fujisawa evaluated
the U.S. market and despite
the foreseen obstacles, they
believed the company could be
successful.
Jerry Kirby, the CEO, had
experienced several recessions
and after the 1980s recession
Kirby thought there was a
better way.
Investigators linked Tylenol to
the cause of eight deaths in
the United States in 1982.
Johnson & Johnson then
decided to pull 31 million
bottles from the shelf.
Akio Morita decided to create the
walkman not based on market
research but his experiences
evaluating what young people
were doing at the time.
Mechanisms of
performance
illustrated
Large and well-
organized knowledge
base
Sensemaking
Situation assessment
and problem
representation
Situation assessment
and problem
representation
Large and well-
organized knowledge
base
Mental simulation
Situation assessment
and problem
representation
Mental simulation
Sensemaking
Sensemaking
Situation assessment
and problem
representation
Example citation
Wozniak and
Smith (2006)
Miller and
Ireland (2005)
Klein (2003)
Crainer (2007)
Nakamura and
Beahmish
(1993)
14 Journal of Management XX(X)
Pattern Recognition. According to the recognition-primed decision (RPD) making model
(Klein, 1993, 1998, 2008), pattern recognition is one of the core processes underlying expert
decision making. Pattern recognition compares an assessment of a situation with past experi-
ences and results in the retrieval of a potential course of action that has been successful in the
past. The expert decision maker identifies environmental cues by viewing the cues as chunks or
patterns (Gobet & Simon, 1996; Simon & Chase, 1973) in order to develop an awareness of his
or her current situation (Behling & Eckel, 1991; Endsley, 1997). Experts use their collection of
complex patterns in their domain to perceive larger and more meaningful patterns in the envi-
ronment more rapidly, as compared to novices who utilize a more deliberate manner of thinking
(Gobet & Simon, 1996). This richer and interconnected knowledge base affords experts the
ability to use pattern matching effectively (Zeitz, 1997).
Once the expert has gained awareness over the situation, he or she begins to engage in pat-
tern matching. That is, they look for a match between past experiences and the current situation
to determine an appropriate course of action. When we say appropriate we mean that the course
of action has been effective in the past. If the decision maker does not find a match between the
current situation and a past state, then the decision maker seeks for more information to fully
develop an understanding of the situation. Because of his or her experience, the expert is able
to perceive patterns that novices cannot (Neisser, 1976). The use of the technique of pattern
recognition allows the expert decision maker to have available to him or her cognitive resources
that can be applied to another purpose. A novice would most likely utilize all cognitive resources
to make sense of a situation because the novice would be unlikely to identify the patterns that
an expert could.
Pattern recognition has been studied extensively. In accounting, the study of pattern recogni-
tion has involved the evaluation of financial trends (Biggs & Wild, 1985; Eggleton, 1982). In
two studies, one that included a student sample (Eggleton, 1982) and one that included experi-
enced auditors (Biggs & Wild, 1985), the results indicated that both samples were able to
identify patterns in financial data and used these patterns to extrapolate current period financial
value. In medicine, it has been determined that physicians use pattern recognition when evalu-
ating patients (Groopman, 2007; Johnson, Hassebrock, Duran, & Moller, 1982) and expert
radiologists are able to make better decisions because they are sensitive to subtle cue configura-
tions in x-ray films that more novice radiologists are unable to detect (Lesgold, Rubinson,
Feltovich, Glaser, Klopfer, & Wang, 1988; Von Hippel, Thomke, & Sonnack, 1999).
Sensemaking. As we have been discussing, experts in their domain are able to make sense of
novel situations. Weick (1995) introduced the process of sensemaking and considered it a cen-
tral cognitive function that occurs in people in natural settings. Individuals engage in
sensemaking when things seem out of the ordinary. Sensemaking is the effort exerted to under-
stand events in order to create order and make sense of what has occurred, what is occurring,
and what will occur (Klein, 1993; Klein, Phillips, Rall, & Peluso, 2007; Weick, 1993). The
process of sensemaking includes problem detection, problem identification, anticipatory think-
ing, forming of explanations, identifying explanations, discovering inadequacies in initial
explanations, and projecting the future.
Klein and colleagues (2007) developed a data-frame theory of sensemaking. They asserted
that individuals engaging in sensemaking utilize the information they receive from a situation
and attempt to fit it in an already developed frame or schema. However, it is when an individual
notices that the schema does not fit that sensemaking and the process of modifying the frame in
order to determine a better solution begins.
Salas et al. 15
Situation Assessment and Problem Representation. Individuals with expertise-based intu-
ition can quickly judge a situation to be familiar or atypical. One technique used to determine
the familiarity of a situation is situation assessment and problem representation. Situation
assessment includes maintaining an understanding of the entire picture. It refers to the identifi-
cation and clarification of the state of a problem (Endsley, 1995). Situation assessment is critical
to a decision maker who is gaining an understanding of the problem situation and attempting to
find similarities to a previously encountered situation. A decision maker may be able to respond
automatically to a situation that he or she determines is familiar, or if the situation is unfamiliar
the decision maker will choose to continue further evaluation of the situation, engaging in such
mechanisms as pattern recognition, mental simulation, and sensemaking.
Randel, Pugh, and Reed (1996) examined the strategies used by electronic warfare techni-
cians in a simulated scenario. Their results suggested that experts place a greater emphasis on
situation assessment while novices emphasized deciding on a course of action. Mosier (1991)
collected data on airline crews performing in a flight simulator and found that most crews reacted
based on assessment of a few critical items of information. Once they started implementation of
their decision they continued to engage in situation assessment to investigate if their decision
was correct. If the crew determined that the decision was incorrect, then most of the remaining
time in the simulator was spent in situation assessment rather than generating alternative solu-
tions. Similarly, Flin, Stewart, and Slaven (1996) collected data on managers of oil platforms and
found that the managers engaged in problem recognition and situation assessment to generate a
solution based on their company’s standard operating procedures.
Automaticity. The more a person practices or experiences a task, the less accomplishing that
task is affected by or affects a concurrent task. Thus, automaticity is developed. Automaticity is
a term used to describe changes in aspects of processes used during task performance as skill
increases. This means recognizing a situation or responding without even realizing how it was
done. The classic domain for showing this is the Schneider and Shiffrin (1977) search task.
Shiffrin and Schneider examined the conditions under which automatic and controlled processes
operate. Their study demonstrated and supported the notion that individuals use controlled pro-
cessing for novel tasks and automatic processes for those tasks that are familiar. Automatic
processes make little impact on our explicit memory, explaining why a task can be accomplished
without any explicit memory of doing the task.
Working memory plays a large role in complex cognition, which is utilized during complex
decision making. For this reason, automaticity is a critical mechanism for performance during
expertise-based intuition. Automaticity contributes to the expert’s ability to understand the larger
meaning of a set of events. That is, because the expert has tremendous experience in a specific
domain, the cognitive resources necessary to make sense of the situation is not spent on what the
decision maker has seen or experienced before. Rather, he or she can concentrate on the novelty
of the situation and expend cognitive resources on understanding these novelties and examining
past experiences that may assist him or her in determining a solution to the problem.
The benefit of automaticity, which leads to having higher working memory capacity, to
the decision maker is that it helps the decision maker by preventing him or her from commit-
ting common decision-making errors such as functional fixedness. Functional fixedness occurs
when there is an inability to use a concept or object in a novel manner. Therefore, what automa-
ticity has been argued to do is allow for a higher working memory capacity that is directly
related to one’s capability for controlled attention. The greater capability for controlled atten-
tion will allow the decision maker to apply cognitive resources toward the aspects of a situation
16 Journal of Management XX(X)
that are novel rather than those aspects that are typical. Therefore, it is the use of automaticity
and the ability of the expert decision maker to apply his or her available cognitive resources to
making sense of the novel aspects of the situation that helps an expert maintain and improve his
or her level of expertise.
Mental Simulation. Another core process that underlies expert decision making is the pro-
cess of mental simulation. Mental simulation provides an evaluation of a course of action to
a situation, specifically, if the course of action “fits” the situation. Klein (2008) argued that
mental simulation is the conscious and deliberate process in which decision makers engage.
Once a decision maker determines that there is a solution for the problem, then the decision
maker evaluates the solution through mental simulation processes. In other words, the deci-
sion maker engages in a simulated implementation of the solution. During this process the
decision maker evaluates the quality of the solution based on what he or she knows about the
situation. The mental simulation then results in adopting the solution as is, modifying the solu-
tion, or determining that further situation assessment or diagnosing is required.
Mental models are critical to the ability of experts to engage in mental simulation. Rutherford
and Wilson (1989) referred to mental simulation as running a mental model. A mental model
mediates the cognitive operations that an expert decision maker engages in to make sense of the
situation. These mental models are based on schema and represent the declarative, procedural,
strategic, and structural knowledge of the expert (Webber, Chen, Payne, Marsh, & Zaccaro,
2000). Mental simulation is the cognitive mechanism that allows the expert to translate his or her
experiences and knowledge, or mental model, into a judgment or a decision (Klein, 2003; Klein
& Crandall, 1995).
Vanharanta and Easton (in press) examined the use of mental simulations in the industrial
marketing context. Their field research provides good evidence that mental simulations do
occur. Vanharanta and Easton emphasized that mental simulation is not the only cognitive pro-
cess that managers engage in, but it is something that they observed that is useful to managers.
Moreover, they recognized the variance in the situations that managers operated in and how
they used or did not use mental simulation. Vanharanta and Easton found that mental simula-
tions had three different roles: (a) They generated awareness of complex business situations
that allowed the manger to complete gaps in information (Klein & Crandall, 1995), (b) they had
a significant role in achieving the desired outcomes, and (c) they helped generate what are
called “down hill” narratives (Tversky & Kahneman, 1981) that connect what is currently hap-
pening to the desired goal state.
Mechanisms of Development
Experience on its own is not sufficient to produce expertise-based intuition. This section pro-
vides a set of processes describing developmental mechanisms of expertise-based intuition.
Deliberate and Guided Practice. Expertise-based intuition is based in extensive experi-
ence within a domain, but certain types of experience are more productive for the development
of this capacity than others. Specifically, experts across a variety of performance domains
(e.g., chess, the performing arts, sports) engage in particular types of practice activities called
deliberate practice to maximize learning. Ericsson et al. (1993) identified four conditions of
deliberate practice: repetition of the same or similar tasks, the presence of immediate feedback
Salas et al. 17
that can guide performance improvements, a task that builds on the learner’s preexisting knowl-
edge, and a learner who is motivated to engage in practice and performance improvement
activities. While Ericsson and colleagues’ theory of deliberate practice is most directly appli-
cable to highly structured tasks, deliberate practice activities of a different form can still occur
in modern organizations where time restraints and other issues interfere with meeting all of the
conditions of deliberate practice. For example, Sonnentag and Kleine (2000) proposed that
activities such as task preparation, seeking feedback, and gathering information from expertise
within a domain are aspects of deliberate practice in modern organizations.
The importance of deliberate practice was demonstrated by Krampe and Ericsson (1996) in
their examination of expert and novice pianists. Their study confirmed findings from Ericsson
and colleagues (1993) that individuals accumulate a large amount of deliberate practice during
the period in which they are working to attain expert performance. Moreover, they argued that
the role of deliberate practice is not limited to the early acquisition phase, rather when expert
level is attained the experts must maintain the observation of deliberate practice to retain their
expert level knowledge.
An expert’s performance level is maintained as a function of experience and deliberate prac-
tice (Ericsson, 2004). As previously mentioned, an expert’s ability to develop automaticity
helps him or her in decision making. However, to maintain that level of expertise it is critical
for an expert decision maker to continue to utilize his or her available cognitive resources to
support continued learning and improvement. Experts use deliberate practice to increase their
control of a situation and their ability to monitor performance in their domain of expertise.
Self-Regulation. Self-regulation is a deliberative process. It involves conscious monitoring and
self-assessment of performance processes. Nonetheless, it is critical to the development of
expertise-based intuition. Experts are better at detecting their errors and understanding why they
occurred (Glaser & Chi, 1988). This helps with continuous performance improvement and the
development of complex knowledge structures and automaticity in the correct forms of perfor-
mance. There are three fundamental types of self-regulation: regulation of the environment,
internal cognitive and affective states and processes, and behavioral performance processes
(Bandura, 1986). Experts engage in these types of self-regulation during preplanning or task
preparation activities (i.e., forethought and structuring of task to be performed), performance
control (i.e., monitoring during performance; Cohen, Freeman, & Thompson, 1998), and post-
performance reflection (Zimmerman, 2006).
The benefit of self-regulation is twofold. Experts use self-regulation during their formative
years, to acquire the requisite knowledge, but also once their skill is mastered, they use self-
regulation to develop their skill and achieve the highest level of skill. For example, Misha
Dichter, a critically acclaimed pianist, uses self-monitoring and self-evaluation to perfect his
style of playing the piano (Mach, 1991). By recording his performances and setting standards,
which he uses to judge himself, he has been able to rethink his process of playing and make the
necessary corrections.
Feedback Seeking. Shanteau (1987, 1992) examined the task characteristics that were
involved when an expert’s performance was observed to be good our poor. One feature present
in good performance of experts was feedback. During the development of an individual’s
expertise it is important that he or she often experience opportunities to receive and respond to
feedback. Sonnentag (2000) found that expert performers in organizational contexts actively
18 Journal of Management XX(X)
sought more feedback from colleagues in comparison to moderate performers in technical jobs
(e.g., software design and engineering). Individuals who want to develop their expertise-based
intuition must proactively seek input from individuals with higher levels of expertise. This is
especially true in environments where the effects of decisions are not immediately available
due to temporal lags or spatial distribution.
Feedback is an essential condition for effective learning. Specifically, feedback that is pro-
vided on a regular basis by an individual who is experienced and knowledgeable in the domain
provides a novice individual with the condition to develop expertise (Cornford & Athanasou,
1995). Fitts (1964) delineated the stages of developing expertise and explained that the cogni-
tive development that enables the development of an expert is related to the information or
feedback that is provided to the individual about personal errors and their successes. It is this
feedback that builds the individual’s level of competence and then is automated (Anderson,
1987; Fitts & Posner, 1967).
In support of feedback as a mechanism of developing expertise, Shanteau and Stewart (1992)
expressed that an accumulation of experiences is not sufficient to develop expertise, but rather
experiences need to include accurate, diagnostic, and timely feedback. Moreover, individuals in
domains where effective feedback is easily obtainable develop their decision-making expertise
better than those individuals in domains that do not. Peiperl (2001) also discussed 360-degree
feedback and its impact on improving performance. That is, she argued that customized and
qualitative feedback is critical to developing individuals into high-level performers.
Motivation. Experience is a necessary condition for developing the complex domain knowledge
underlying expertise-based intuition; however, it is insufficient on its own (Ericsson et al., 1993).
Experience does not directly produce expertise. It takes focus and dedication to understand expe-
rience and improve performance. This requires high levels of motivation sustained for long
durations of time. Across domains, experts are characterized as having this drive to master their
chosen field. Winner (1996) referred to this as the “rage to master.” In addition to the deliberate
practice account of acquiring expertise described earlier, which presupposes a high level of moti-
vation on the part of the developing expert, other theoretical accounts emphasize these
characteristics of experts as well. For example, Sternberg’s (1998a) model of developing exper-
tise identified motivation as the core driver of development and Glaser (1996) identified the shift
in agency for learning and performance improvement from external sources to internal as a hall-
mark of the expert’s developmental process.
Four issues related to motivation have been identified as particularly relevant to the devel-
opment of expertise: self-efficacy beliefs, goal orientations, motivation rooted in drive for
success not fear of failure, and intrinsic motivation of the domain (Zimmerman, 2006). First,
experts have high levels of self-efficacy, which leads to setting higher goals and increased
levels of commitment to those goals (Cleary & Zimmerman, 2001). Second, experts focus on
the processes of performance and value improvement and learning; they have learning goal
orientations (Winner, 1996). Third, experts tend to be motivated by achievement and success
and not by a fear of failure. They do not focus on what could happen if they fail and instead
maintain focus on the positive outcomes of success. Fourth, experts are intrinsically motivated
by their domain. Not all practice activities are inherently enjoyable, but experts tend to value
tasks within their domain and continue practicing in the absence of extrinsic rewards for doing
so (Karniol & Ross, 1977; Kitsantas & Zimmerman, 2003). In addition, it has been proposed
that motivation is central to an expert’s ability to adapt expertise to a novel situation (Eccles
& Feltovich, 2008).
Salas et al. 19
Goal Setting. Goal setting has been one of if not the most researched theories in our field
(Mitchell & Daniels, 2003). Researchers have examined, demonstrated, and reviewed the ben-
efits of goal setting (Locke & Latham, 1990; Locke, Shaw, Saari, & Latham, 1981; Mitchell,
1997). The manner in which goal setting can help to develop expertise is by providing the deci-
sion maker with focus and assisting him or her in developing task strategies. Seijts and Latham
(2005) outlined the mechanisms for which goal setting helps increase an employee’s effective-
ness. We elaborate on a few of those mechanisms to explain how goal setting can help develop
expertise-based intuition.
Goal setting can help by focusing the decision maker’s attention on actions of the task that
will be goal relevant. Therefore, actions that are not pertinent to the goal will become obsolete
and useless. Furthermore, goal setting is beneficial to developing the decision maker cogni-
tively by helping him or her develop effective task strategies. For example, research done at the
Weyerhaeuser Company by Latham and Saari (1982) found that truck drivers drew on their
existing knowledge to develop improved strategies for them to work smarter, and not harder,
which allowed them to meet their high-performance goals.
The use of performance goals is most effective when an individual has the knowledge neces-
sary to develop different task strategies. That is, when a decision maker is no longer considered
to be a novice, then utilizing performance goals to develop his or her expertise is effective.
However, when a decision maker is still developing his or her knowledge base, the use of
performance goals are not practical, but rather the use of learning goals will help the decision
maker gain the knowledge that is lacking. Research has demonstrated that the benefit of goal
setting for tasks where the individual has had minimal prior learning or experience exists more
for knowledge acquisition, environmental scanning, and seeking feedback (Seijts, Latham,
Tasa, & Latham, 2004). In other words, experts can benefit from performance goals because it
focuses them on the distal goal. On the other hand, learning goals help develop the knowledge
(e.g., declarative and procedural) that a novice is lacking.
Expertise-Based Intuition in Teams
In the previous section we presented both mechanisms of performance, which expert decision
makers engage in when making decisions, and mechanisms for development of expertise-
based intuition. These mechanisms were presented and based at the individual level; however,
decision making frequently occurs in team settings. Just as individuals develop performance
adaptations to reach high levels of effectiveness, team researchers have identified a set of
expertise-based team mechanisms (Salas, Rosen, Burke, Goodwin, & Fiore, 2006). Similar
to the mechanisms presented at the individual level, these team-level mechanisms represent a
prototype of team-based expertise. We do not argue that all are equally important for teams
when making decisions; rather, the importance of any one mechanism is dependent on the
features of the specific task and situation.
A few of the mechanisms presented in the previous section at the individual level can apply
to the team level. Critical to these team-level mechanisms and developing team-based exper-
tise is that the team functions as a team; that is, the team engages in the basic teamwork
competencies (see Salas, Sims, & Burke, 2006). The mechanism of pattern recognition is
equally as important at developing expertise-based intuition at the team level as at the indi-
vidual level. For example, take the members of a top management team where the individuals
contain different levels of expertise and in any given context most likely will recognize
20 Journal of Management XX(X)
different patterns. It is inherent that the recognition of a pattern comes from an individual.
However, the richness of the interpretation of the recognized patterns is contingent on the con-
versational process with others. From these conversations different patterns may be recognized,
weaknesses of proposed courses of action may be identified, and gaps of information may be
filled by other team members.
The mechanism of deliberate and guided practice is not only central for the development of
expertise for individuals but also for teams. Again, the impact of deliberate and guided practice
at the team level is based on the fact that the team has experience working as a team. Therefore,
teams need to spend time working together in order to understand the function of their team and
their roles. It is through this team-level deliberate and guided practice that teams develop shared
mental models. Similarly, situation assessment and problem representation is critical at the
team level. Expert teams’ assessment of a situation should be more advanced than novice teams.
The novelty of a situation should be more quickly identified. Also the synthesis of the pieces of
information gathered from identifying similarities of the current situation to other events should
be executed more quickly. Additional mechanisms at the team level are briefly reviewed next.
Learn and Adapt. It is no surprise that today’s organizations are characterized by changing,
dynamic environments. To accommodate for these environments, there is a need to develop
expertise-based intuition. Similar to developing intuition on the individual level, at the team
level teams must learn and adapt in these dynamic contexts to develop their team expertise-based
intuition. Individuals practice self-regulation and engage in continuous learning to develop and
maintain performance capacities. Teams must do the same. Research has examined how teams
best learn and adapt. Edmondson, Bohmer, and Pisano (2001) demonstrated that surgical teams
who supported the collective learning process successfully implemented new technology sol-
utions. Interdependence among the team and a dynamic environment requires individuals to
communicate and coordinate to create new solutions; this process is called the collective learn-
ing process. A team may accomplish this by learning about others’ roles (Levine & Moreland,
1999), improvising (Orlikowski & Hoffman, 1997), and making small adjustments to existing
performance strategies (Leonard-Barton & Deschamps, 1988).
A team’s ability to adapt to novel situations is critical and inherent in expert teams. Burke,
Stagl, Salas, Pierce, and Kendall (2006) provided a multidisciplinary, multilevel, and multi-
phase conceptualization of team adaptation. The model of team adaptation consists of four
process-oriented phases: (a) situation assessment, (b) plan formulation, (c) plan execution, and
(d) team learning. Their model illustrates the series of phases that unfold over time and consti-
tute the core processes (and emergent states) that underlie adaptive team performance.
C. S. Burke and colleagues included in their model the task and team expertise of the team
members. They argued that this expertise contributes to the development of accurate mental
models, which then become flexible as teams become adaptive.
Clear Roles and Responsibilities. Similar to expertise-based intuition at the individual
level, expertise-based intuition at the team level means that the expert team is comprised of
individuals who have a large knowledge base. However, an expert team is not merely a group
of experts. Team members must have well-developed metacogntion. The individuals must
know what their role is and what they know, as well as know what each team member knows
and their roles. This knowledge is crucial to their ability to anticipate each other’s actions and
needs. Nevertheless, because the likelihood that the situations where expertise-based intuition
Salas et al. 21
will be used is in a dynamic and likely novel context, team members must not allow their
defined roles to prohibit them from learning and adapting as a team. The roles of the members
must be clear, but they should not be seen as rigid. When demands arise from a situation where
roles may need to mold together or new roles may be required, then the team must adapt to the
necessities of the specific situation to perform well. Research conducted by Eisenstat and
Cohen (1990) found that the highest performing top management teams were characterized by
an ability to accept whatever changes to team member roles occurred as long as these roles
were clearly articulated.
Prebrief and Debrief Cycle. Teams who engage in a prebrief and debrief perform at higher
levels (Smith-Jentsch, Cannon-Bowers, Tannenbaum, & Salas, 2008). A prebrief and debrief act
as a mechanism for decision-making effectiveness. Both types of briefs shift the emphasis on the
performance session from outcome to process, both in the short term and in the long term. A
prebrief provides an opportunity to ensure that all team members have a clear understanding of
all team members’ roles and responsibilities. Moreover, it provides the leader with an opportu-
nity to present the team with any information that will help the team’s performance in the
specific situation (Inzana, Driskell, Salas, & Johnston, 1996). The debrief is also critical to the
development of expertise. People learn through experience when the experience is followed
by meaningful, diagnostic feedback. The debrief provides this feedback. Effective debriefing
involves meaningful reflection and facilitated self-evaluation by team members regarding
their own performance and open discussion of performance successes and gaps (Prince, Salas,
Brannick, & Orasanu, 2005; Smith-Jentsch et al., 2008).
Strong Team Leaders. Leaders serve many functions. For a team, effective team leadership
represents a characteristic of successful team performance. Two critical functions of an expert
team’s leader are that they must be task experts and have exceptional leadership skills. The
role of a team leader is not limited to one individual. On the contrary, we agree with Zaccaro,
Rittman, and Marks (2001) that as teams become more experienced and develop their exper-
tise a team member or several team members may take over more of the leadership functions
of the team. However, the designated or external leader still maintains his or her boundary-
spanning responsibilities. That is, the external leader ensures that the strategic link between
the team and the organization is developed and maintained. This link provides resources and
support that are critical to the success of the team (Cordery & Wall, 1985; Druskat & Wheeler,
2003; Hackman, 1986).
Strong Sense of Team Affect and Orientation. Our discussion of the use of expertise-based
intuition has been rooted in the fact that expertise-based intuition will most likely be applied and
most effective in contexts that are dynamic and uncertain (Khatri & Ng, 2000). These dynamic
and uncertain environments pose a threat to team-level affect constructs. Much research has
examined how team-level affect does impact performance processes and outcomes. For exam-
ple, Edmondson (1999) found that psychological safety, the shared belief that the team is safe for
interpersonal risk taking, fosters learning behavior in work teams. Additional research has found
that mutual trust (Bandow, 2001), collective efficacy (Gibson, 2003), and collective orientation
(Driskell & Salas, 1992) are critical for team effectiveness. Teams operating under dynamic and
uncertain conditions will likely experience tension that has the potential of impacting their sense
of team orientation. An effective team will maintain a high level of team orientation, and if it is
22 Journal of Management XX(X)
threatened an effective expert team will engage in appropriate conflict management. Under-
standing how to manage conflict effectively and quickly is critical for a team’s effective use of
expertise-based intuition in dynamic and complex settings.
Coordination and Cooperation. Teamwork is grounded in the notion of cooperation and
coordination. For a team to develop its expertise-based intuition, a team must be able to orga-
nize team members’ activities to successfully reach their goals (Cannon-Bowers, Tannenbaum,
Salas, & Volpe, 1995). Each team-based decision or action will require the coordination and
cooperation of each team member. Kozlowski and Bell (2003) defined coordination as the
timeliness of actions and contributions by all team members. Argote and McGrath (1993) out-
lined that integral to coordination is the integration of team member actions together with an
appropriate temporal pacing of these actions within the context.
Team research has also identified the importance of cooperation. Wagner (1995: 152) defined
cooperation as the “willful contribution of personal efforts to the completion of interdependent
jobs.” Cooperation has been shown to be associated with team effectiveness. Effective coopera-
tion necessitates a high degree of involvement from the team members. Contexts in which
expertise-based intuition will be most effective require the involvement of all individuals, in part
because of the interdependent nature of the task, but also because of the nature of the environ-
ment (e.g., dynamic, uncertain, changing goals, etc.).
Future Directions
The preceding material as well as other recent reviews on intuition has illustrated a strong and
growing science of intuition. This is a topic of great importance to organizations as intuition can
be a great source of effectiveness in the case of expertise-based intuition. Although there is a
strong basis to support these claims and to guide organizations in developing and implementing
programs to build and manage this capacity, further research is needed. This section outlines
four key research needs that must be met if organizations are to make the most out of the intu-
itions of their managers and their employees.
First, a deeper understanding of how expertise-based intuition functions on the team level is
needed. Decision making in organizations is a multilevel phenomenon in most cases. This raises
interesting questions in the area of intuitive decision making. Specifically, how do people share
or communicate their intuitions if they are not immediately defensible in a rational sense? Sev-
eral interesting lines of research have begun to address components of this problem. For example,
Von Glinow, Shapiro, and Brett (2004) outlined how nonverbal methods of communication can
be used in team settings. Their specific model addresses managing emotional conflict in multi-
cultural teams; however, this type of approach may be useful for sharing other types of nonverbal
information (i.e., intuitions) in group settings. In addition, Hodgkinson, Sadler-Smith, Burke,
Claxton, and Sparrow (2009) discussed the importance of team composition in terms of cogni-
tive processing styles. In addition, team composition in terms of level of expertise will likely
have a great impact on how the team shares intuitions. For example, the U-shaped developmen-
tal model of intuition described earlier suggests that both novices and experts are more likely to
have strong intuitions and a midrange of development characterized by few intuitions. Multi-
level models of expertise-based intuitive decision making are needed to address these types of
questions. Subsequently, these models can drive interventions for developing team-level pro-
cesses for maximizing the effectiveness of this type of decision making.
Salas et al. 23
Second, a better understanding of how deliberation and intuition interact is needed. Dual
processing theories propose two parallel and interacting cognitive systems. This review has
discussed conditions under which people will be more likely to use (or effective when they use)
intuition over analysis or vice versa. However, in the context of decision making in organiza-
tions (and especially in group settings), the use of intuition and analysis is not an either/or
proposition. Decision making most commonly involves both. Consequently, theoretical models
need to account for this interaction and provide guidance for how decision makers should
manage these types of processing.
Third, more rigorous studies in the field are needed. The majority of the literature reviewed
in this article has been from either basic science laboratory studies or from descriptive field
studies. The convergence between these literatures is encouraging; however, each has its own
set of limitations (i.e., external validity issues with laboratory studies and internal validity
issues with field studies). Empirical field research focused on testing models of individual- and
team-level expertise-based intuition is needed. This brings to bear the methods that are used to
investigate expertise-based intuition in the field. Methodology such as think-aloud protocols,
narratives, or shadowing may deem insightful in unpacking the black box of intuition. In addi-
tion, systematic longitudinal evaluations of interventions designed to develop expertise-based
intuition need to be conducted.
Fourth, comprehensive strategies for developing expertise-based intuition are needed. A good
deal is known about how to improve intuition over time. This review has presented what has
emerged from various disciplines on how expert decision makers acquire effective intuition.
However, many of these techniques have been used in isolation and few (if any) comprehensive
programs targeting the development of intuition have been developed, implemented, and evalu-
ated. Thus, it would be fruitful to examine this for the development of executives and managers
in critical decision-making positions.
Concluding Remarks
This review began by posing the general question of whether or not intuition as a psychological
construct has value for organizations. We believe that the literature reviewed previously clearly
indicates that it does. Intuition plays a major role in the decisions people make. There is a vari-
ety of theoretical models and empirical data suggesting that intuition is a real phenomenon and
contributes to effectiveness, especially in situations where it counts (e.g., time-pressured com-
plex decision making in the real world). This review has merely outlined some of the core
contributions and existing knowledge available for organizations to draw on. Although there is
compelling research on how intuition works, the conditions under which it works best, and how
to improve intuitive expert decision making, there is much work to be done. This includes
research aimed at developing and evaluating strategies and interventions for training and the
support of intuitive decision making. In addition, broader issues of the design of socio-technical
systems and organizational structures that capitalize on the power of intuitive thinking and
protect against its pitfalls are in need of investigation. For example, the individual differences
perspective on processing style should be investigated within a team composition framework
(Hodgkinson, Sadler-Smith, Sinclair, & Ashkanasy, 2009).
The time for a science of intuition in organizations capable of guiding practice and improving
effectiveness has come. Although the label intuition is frequently ascribed some transcendental
quality, the phenomenon is real. It is important to organizational effectiveness and the management
24 Journal of Management XX(X)
sciences to contribute to the practice through more and rigorous research into the nature and
development of intuitive decision-making skills. Intuition is how people rapidly detect coher-
ent patterns in complex environments. It is how they generate solutions that work (cf.
mythically optimal solutions) without the luxury of limitless time. In addition, expert (or knowl-
edge-based) intuition can be acquired through experience. All of these factors indicate that the
management sciences should pay more attention to the broader range of cognitive processing
happening in organizational contexts.
Authors’ Note
The views expressed in this article are those of the authors and do not necessarily reflect University of
Central Florida or Defense Advanced Research Projects Agency (DARPA). This work was partially sup-
ported by the Aiding Decision Making Through Intuition project funded by the Defense Advanced
Research Projects Agency to the Institute for Simulation & Training at the University of Central Florida
(Contract No. NBCH080101; Denise Nicholson, principle investigator).
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