Journal of Management Vol. 36 No. 4, July 2010 941-973 DOI: 10.1177/0149206309350084 © The Author(s) 2010

Expertise-Based Intuition and Decision Making in Organizations Eduardo Salas Michael A. Rosen Deborah DiazGranados University of Central Florida

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

Acknowledgments: 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 supported 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, principal investigator). Corresponding author: Eduardo Salas, 3100 Technology Parkway, Orlando, FL 32826, USA E-mail: [email protected]

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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. However, 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 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 sciences 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. Relying 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 decision 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 expertisebased intuition perspective. 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 naturalistic 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

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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 science of intuition with the aim of clearly defining expertise-based intuition. Several recent and comprehensive reviews have addressed general issues of conceptual clarification and synthesis 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.

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 (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 knowledge 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

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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 expertise. 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 intuition.” 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 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 experience. 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 intuition 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,

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Figure 1 Venn Diagram Depicting the Overlap and Distinction Between the Constructs of Intuition and Expertise

1994; holistic and analytic, Nisbett, Peng, Choi, & Norenzayan, 2001; reflexive and reflective or X and C systems, Lieberman, Jarcho, & Satpute, 2004; associative and rule-based, Sloman, 1996; conscious and unconscious, Dijksterhuis & Nordgren, 2006; intuitive and 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 cognition 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

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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 acq­ uired 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 properties 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 override 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 selection. 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) proposed 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. 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 processing 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

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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 conceptualization, 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 supposedly 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 reasoning strategies (Dreyfus & Dreyfus, 1986). These findings gave rise to the knowledgebased 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 generalizable descriptors of expert performance mechanisms. Instead, expertise is viewed as adaptation to the task constraints (Ericsson & Lehman, 1996), and the broad array of performance mechanisms 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 influence 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.

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 intuition is likely to be accurate or inaccurate is especially important in situations where

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Table 1 Primary Factors Influencing the Use and Effectiveness of Intuition in Decision Making for Individuals Factors influencing use and effectiveness of intuition Decision maker

Expertise

Processing styles Decision task

Task structure

Availability of feedback Decision environment

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 TeschRomer (1993) Lipshitz, Klein, Orasanu, and Salas (2001)

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 environment 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

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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 learning 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 previously, 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 taxonomies 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 individual 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 individual 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 Cognitive 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 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, 2008a). 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), Hodgkinson et al. (2008), and Hodgkinson and SadlerSmith (2003). 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)

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proposed that the effectiveness of intuition will differ based on whether or not the decisionmaking task is intellective or judgmental in nature (Laughlin & Adamopoulos, 1980). Specifically, intuition will be more effective in judgmental tasks. 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 criterion 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 utility 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 industries. 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 intuition 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 effectiveness 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

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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 experienced 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.

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 understand “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 development 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 intuitive 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 provided 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 literature 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

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Table 2 Mechanisms of Performance for Expertise-Based Intuition, With Empirical Support Mechanisms of performance

Description

Key points

Example citation

Large and welldeveloped knowledge base

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.

• Experts organize knowledge in a conceptual way. • Experts organize knowledge with more interconnections between concepts. • Knowledge is organized via semantic networks, theories, and schemas.

Bordage and Zacks (1984); Chi and Ohlsson (2005); Feltovich, Johnson, Moller, and Swanson, (1984); Markman (1999)

Pattern recognition

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.

• Experts view cues as chunks or patterns. • Experts’ use of pattern recognition allows them to assess the environment more rapidly than novices. • Affords the ability to use pattern matching effectively.

Biggs and Wild (1985); Eggleton (1982); Gobet and Simon (1996); Neisser (1976); Simon and Chase (1973)

Sensemaking

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.

• Experts engage in problem detection, identification, anticipatory thinking, forming of explanations, identifying explanations, discovering inadequacies in initial explanations, and projecting the future.

Klein (1993); Klein, Phillips, Rall, and Peluso (2007); Weick (1993, 1995)

Situation assessment and problem representation

Expertise-based intuition utilizes situation assessment and problem representation, which includes maintaining an understanding of the entire picture.

• Quick judgments can be made of the situation (e.g., atypical or familiar). • Identification and clarification of the state of a problem.

Endsley (1995); Randel, Pugh, and Reed (1996); Mosier (1991); Flin, Stewart, and Slaven (1996)

Automaticity

The process by which an individual can accomplish a task without using all cognitive resources.

• 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.

Shiffrin and Schneider (1997)

Mental simulation

Provides an evaluation of a course of action to a situation, specifically if the course of action “fits” the situation.

• 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.

Klein (2008); Rutherford and Wilson (1989); Klein and Crandall (1995)

declarative knowledge (i.e., facts). Experts organize their knowledge in a more conceptual way (Bordage & Zacks, 1984; Chi & Ohlsson, 2005), with more interconnections 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,

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Table 3 Mechanisms to Develop Expertise-Based Intuition, With Empirical Support Mechanism of development

Key points

Example citation

Deliberate and guided practice

• 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

Ericsson (2004); Ericsson, Krampe, and Tesch-Romer (1993); Krampe and Ericsson (1996); Sonnentag and Kleine (2000)

Self-regulation

• 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

Bandura (1986); Cohen, Freeman, and Thompson (1998); Glaser and Chi (1988)

Feedback seeking

• Feedback must be available to develop expertise • Experts seek out feedback • Feedback is essential for effective learning

Shanteau (1987, 1992); Sonnentag (2000); Cornford and Athanasou (1995)

Motivation

• Experts have a “rage to master” • Is the key driver of expertise development • Internal need for learning and performance improvement

Cleary and Zimmerman (2001); Glaser (1996); Sternberg (1998a); Winner (1996)

Goal setting

• 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

Locke and Latham (1990); Locke, Shaw, Saari, and Latham (1981); Mitchell (1997); Seijts, Latham, Tasa, and Latham (2004)

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 experience 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 knowledge representations in that they are irregular collections of fragments (diSessa, 1988, 1993; Smith, diSessa, & Roschelle, 1993) and not organized in a manner

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Table 4 Business Examples of the Use of Expertise-Based Intuition Business example

Key points

Mechanisms of performance illustrated

Example citation

Apple: Development of the computer Apple I

Steve Jobs provided the knowledge that he had gained from his experience in the industry to develop the PC board.

Large and well-organized knowledge base

Wozniak and Smith (2006)

Honda: Entering the U.S. motorcycle market

Two scouts appointed by Takeo Fujisawa evaluated the U.S. market and despite the foreseen obstacles, they believed the company could be successful.

Sensemaking Situation assessment and problem representation

Miller and Ireland (2005)

Citizens Federal Bank: Change in business strategy to a mortgage bank

Jerry Kirby, the CEO, had experienced several recessions and after the 1980s recession Kirby thought there was a better way.

Situation assessment and problem representation Large and well-organized knowledge base Mental simulation

Klein (2003)

Johnson & Johnson: Pulling Tylenol from the shelves after cyanide poisoning scare

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.

Situation assessment and problem representation Mental simulation Sensemaking

Crainer (2007)

Sony: The creation of the Walkman in 1979

Akio Morita decided to create the walkman not based on market research but his experiences evaluating what young people were doing at the time.

Sensemaking Situation assessment and problem representation

Nakamura and Beahmish (1993)

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 networks 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. 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 represented 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,

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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). 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 experiences 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 environment 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 pattern 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 recognition 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 experienced 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 evaluating patients (Groopman, 2007; Johnson, Hassebrock, Duran, & Moller, 1982) and expert radiologists are able to make better decisions because they are sensitive to subtle cue configurations 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 central

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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 understand 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 thinking, 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. Situation Assessment and Problem Representation Individuals with expertise-based intuition 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 identification 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 technicians 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 solutions. 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

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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 processing 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 committing 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 automaticity 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 attention will allow the decision maker to apply cognitive resources toward the aspects of a situation 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 process 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 decision maker engages in a simulated implementation of the solution. During this process the decision maker eva­ luates 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 solution, 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,

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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 process 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 simulations 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 happening to the desired goal state.

Mechanisms of Development Experience on its own is not sufficient to produce expertise-based intuition. This section provides a set of processes describing developmental mechanisms of expertise-based intuition. Deliberate and Guided Practice Expertise-based intuition is based in extensive experience 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 that can guide performance improvements, a task that builds on the learner’s preexisting knowledge, 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 applicable 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.

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An expert’s performance level is maintained as a function of experience and deliberate pra­ctice (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 performance. 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 postperformance 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 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 provided 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 &

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Athanasou, 1995). Fitts (1964) delineated the stages of developing expertise and explained that the cognitive 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 decisionmaking 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 experience 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 motivation 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 expertise 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 hallmark of the expert’s developmental process. Four issues related to motivation have been identified as particularly relevant to the dev­elopment 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).

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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 benefits 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 decision 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 effectiveness. 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 cognitively 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 necessary 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 exp­ erience 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

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expertise 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 individual 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 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 conversational 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 sol­utions. Interdependence among the team and a dynamic environment requires individuals to communicate and coordinate to create new solutions; this process is called the collective learning 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 multiphase 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 constitute 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

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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 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 opportunity 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

964    Journal of Management / July 2010

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 expertise 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 res­ ponsibilities. 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 example, 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 threatened an effective expert team will engage in appropriate conflict management. Understanding 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 organize 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) outlined 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 cooperation 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 environment (e.g., dynamic, uncertain, changing goals, etc.).

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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 intuitions 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? Several 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 multicultural 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 cognitive 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 developmental 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. Multilevel 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 processes for maximizing the effectiveness of this type of decision making. 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 organizations (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 individualand 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 addition, systematic longitudinal evaluations of interventions designed to develop expertise-based intuition need to be conducted.

966    Journal of Management / July 2010

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 evaluated. 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 variety 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 complex 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 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 coherent 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 knowledge-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.

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