Intuition in Management
Summary and Keywords
An extensive literature has accumulated during the past three quarters of a century on the topic of intuition in management. The beginnings of management intuition scholarship are to be found in Chester Barnard’s insightful speculations on the role and significance of logical and non-logical processes in managerial work. Barnard’s thinking impacted profoundly Herbert Simon’s foundational concept of bounded rationality, which shed much needed light on how managerial decision-making is accomplished in real-world settings by using intuition as well as analysis. In parallel, management researchers in common with scholars in a wide range of applied fields also drew on Daniel Kahneman, Amos Tversky, and colleagues’ seminal behavioral decision research and its focus on the systematic errors and biases that accrue in managers’ intuitive judgments as the result of the use of heuristics (e.g., representativeness, availability, anchoring and adjustment, and affect heuristics). In recent years management researchers have drawn on further insights from Klein and colleagues’ work in naturalistic decision-making (NDM) (e.g., the “recognition primed decision-making model,” RPD) to conceptualize managerial work as expert performance and in understanding expert-versus-novice differences using the “skill acquisition model” (SAM). In recent years managerial intuition research has alighted on the dual-process theories of Epstein, Evans, Stanovich, and others as a conceptual foundation for further theorizing and research in terms of System 1 (also referred to as Type 1) and System 2 (Type 2) processing. More recently still, research in neurology (e.g., the “somatic marker hypothesis”) and social cognitive neuroscience (e.g., the specification of complementary “reflexive (X)” and “reflective (C)” systems) has mapped the physiological and neural correlates of intuitive processing and begun to inform not only intuition research but decision research more widely in management and organization studies. These various developments have shed light on how intuitive decision-making is accomplished in managerial work across diverse management subfields including entrepreneurship, business ethics, human resources, and strategic management. More recently, scholars are turning to paradox theory and process philosophy as alternative ways of viewing the phenomenon of intuition in organizations.
Intuition has held an allure for managers and management scholars ever since the conception of “administrative science” in the 1930s, most notably in Chester Barnard’s The Functions of the Executive (1936). The topic of intuition in decision-making gained increased traction and impetus through the latter half of the 20th century once researchers realized and acknowledged that rational approaches, invaluable though they are, have limits in the uncertain and dynamic world that managers work in and that the classical economic assumptions of rationality and “economic man” (Homo economicus) did not fare well under detailed scrutiny. This observation was articulated most famously by Nobel laureate Herbert Simon with his concept of bounded rationality, based on the precepts that in real-world decision-making the number of alternatives to be explored and the amount of information required is often large while the human brain’s information processing capacity, by comparison, is limited. A corollary of this is that to be able to take decisions under conditions of dynamism, uncertainty, and time pressure, experienced managers are able to, and often must, resort to means other than rational analysis. It is with the acceptance of this fundamental precept that management research has embraced intuition as a vital force in managerial life, both with respect to intuitive judgments as sources of errors and biases (thus emphasizing the “downside” of intuition, as is in the heuristics and biases programme of research) and in the recognition that in particular environments and under certain sets of conditions, intuition is an appropriate and effective means of taking decisions (thus emphasizing the “upside” of intuition, as in “intuitive expertise”). Intuition is a viable basis for action in a managerial world that is not only replete with paradoxes but is also constantly on the move.
Three important milestones in the history of management intuition research are singled out for the purposes of the introduction to this article (for a detailed historical review and an overview of developments, see, respectively, Akinci & Sadler-Smith, 2012; Sadler-Smith & Burke-Smalley, 2015). These milestones are (1) Chester Barnard’s distinction between “logical processes” and “non-logical processes” (Barnard, 1938); (2) Herbert Simon’s model of “bounded rationality” (Simon, 1955); and (3) Kahneman and Tversky’s concept of “heuristics and biases” (Tversky & Kahneman, 1974). In the account offered here a direct line is discerned from Chester Barnard through Herbert Simon to Daniel Kahneman and Amos Tversky and thenceforward to Gary Klein’s recognition-primed decision model of intuitive expertise as well as insights from contemporary neurology and neuroscience.
Chester Barnard’s Distinction Between Logical and Non-Logical Processes
Chester Irving Barnard (1886–1961) was an intellectually curious executive at the American Telegraph and Telephone (AT&T) Company. His main contribution to intuition research is to be found in the appendix to his book The Functions of the Executive (1938). This book aimed to offer an account from the point of view of a practitioner of how management was conducted; it was an alternative to the prescriptive approaches and scientific management principles prevalent in U.S. business schools at the time. The appendix, based on the lecture that Barnard gave to the engineering faculty and students of Princeton University on March 10, 1936, was entitled “The Mind in Everyday Affairs.” In it he drew a distinction between two types of mental processes: “logical” (“conscious thinking which could be expressed in words, or other symbols, that is, reasoning”) and “non-logical” (“those [mental processes] not capable of being expressed in words or as reasoning, which are only made known by a judgment, decision or action”) (Barnard, 1938, p. 302). In focusing on this distinction, Barnard compared the work that executives (managers) do compared to that, say, of scientists. Executives, unlike scientists, do not typically enjoy the luxury of taking decisions based on ordered and leisurely rational analyses; instead they depend to a large extent on their experience and are often compelled to use intuition to respond to situations requiring fast decision-taking and complex judgments (which is not to say that scientists do not use intuition in the process of scientific discovery, they do; see Sadler-Smith, 2015). Barnard described intuition, which is essentially “good judgment,” as being grounded in knowledge and experience “mostly impressed upon us unconsciously or without conscious effort” (Barnard, 1938, p. 305). Because they are so complex and so rapid—often approaching instantaneous—the non-logical (intuitive) processes themselves “cannot be analysed by the person within whose brain they take place consisting, as they do, of a mass of patterns, concepts, techniques, and abstractions that increase in number and complexity with directed experience, study and education” (Barnard, 1938, p. 305).
Herbert Simon’s Model of Bounded Rationality
The focal point of Simon’s contribution to intuition research and to administrative science more generally is his seminal concept of “bounded rationality” (Simon, 1955). The model is captured comprehensively in Simon’s magnum opus, Administrative Behaviour, first published in 1947. In this book numerous references to Barnard attest to his impact on Simon’s thinking. The model of bounded rationality is premised on the assumption that managerial decision-making is intendedly, but not wholly, rational—that is it is boundedly rational. Boundedly rational actors make choices that are “good enough” and thereby “satisfice” (i.e., choosing the first acceptable alternative, rather than any acceptable alternative). This, Simon reasoned, was because the computational demands of maximizing, according to classical economic principles, are an unrealistic standard for human judgment given the constraints imposed by human beings’ computational capabilities and the limits of the available information (Gilovich & Griffin, 2002). This departure from economic rationality, in which utility maximization was replaced with satisficing, was the groundwork for Simon’s research with William Chase. Chase and Simon conducted a series of experiments that explored the cognitive bases of expert/novice differences in chess. This work is a historical antecedent of pattern recognition–based theories of intuition (see section on “Expert Intuition”). In keeping with Barnard’s original exposition, Simon argued that the term “intuition” may be used to describe decision-making behavior that is speedy and domain-specific and for which the expert is unable to describe in detail the reasoning or other processes that produced the often-correct response. For Simon intuition is “nothing more and nothing less than recognition” (Simon, 1992, p. 155), and intuitions are “analyses frozen into habit and the capacity for rapid response through recognition” (Simon, 1987, p. 63). In recognition of his pioneering research into decision-making processes in organizations, Herbert A. Simon (1916–2001) was awarded the Nobel Prize in Economics in 1978.
Kahneman and Tversky’s Model of “Heuristics and Biases”
In the late 1960s and early 1970s Daniel Kahneman (b. 1934) and Amos Tversky (1937–1996) explored the systematic biases that accrue in human judgment as a result of intuitive errors. These errors stem from inherent fallacies and miscomputations in human information processing (Kahneman & Tversky, 1973; Tversky & Kahneman, 1974). Kahneman and Tversky built on Simon’s work by, in effect, obtaining a “map” of bounded rationality (i.e., conditions and constraints under which human judgment fails) (Kahneman, 2003, p. 1449). Their work is seminal in the field of behavioral decision theory (BDT), and, like the concept of bounded rationality, its importance is hard to overstate.
In what has come to be known as the “heuristics and biases” research program, Kahneman and Tversky defined intuition as “thoughts and preferences that come to mind quickly and without much reflection” (Kahneman, 2002, p. 449). In a series of experiments Kahneman, Tversky, and colleagues demonstrated that although mental shortcuts (“heuristics”) sometimes succeed and sometimes fail, under the conditions of uncertainty which defined this stream of work, intuitive judgments (i.e., heuristics) are wrong more than they are right and as a result produce errors in reasoning and judgment. The three main heuristics identified by Kahneman and Tversky from their various lab studies are: (1) the representativeness heuristic (i.e., judgment based on “what is typical”); (2) the availability heuristic (i.e., “what comes easily to mind”); and (3) the anchoring and adjustment heuristic (i.e., “what happens to come first”) (Kahneman, 2002, 2003; Kahneman, Slovic, & Tversky, 1982).
The concept of heuristics and biases revolutionized research on judgment and decision-making, and its influence quickly spread beyond psychology into fields as diverse as medicine, politics, law, economics, and business administration (see Gilovich, Griffin, & Kahneman, 2002, for a collection of formative articles in BDT). Other important elaborations on this theory include the “affect heuristic,” i.e., a reliance on rapid, automatic feelings to guide judgment, where “affect” refers a feeling state (with or without consciousness) that marks a stimulus as “good” (positive valence) or “bad” (negative valence) (Slovic, Finucane, Peters, & Macgregor, 2002). In recognition of his contribution for having integrated insights from psychological research into economic science, especially concerning human judgment and decision-making under uncertainty, Daniel Kahneman was awarded the Nobel Prize in Economics in 2002. Tversky did not receive a Nobel Prize since it cannot be awarded posthumously, but his contribution was cited prominently by the Nobel committee.
As an important aside at this point, a different category of heuristic must be mentioned which is often ascribed to “gut feelings” or intuition. The fundamental principle of the so-called “fast and frugal” heuristics studied by Gigerenzer and colleagues is that decisions which are based on simple and explicit rules (such as rules relating to recognition) can be as effective as more time-consuming, exhaustively rational approaches that endeavor to acquire and use all the available relevant information (Gigerenzer, 2008; Gigerenzer, Todd, & ABC Research Group, 1999). The simplest of these, the “recognition heuristic,” is a quick and economical way of choosing between two alternatives and can be stated thus: “if one of two objects is recognized and the other is not, then infer that the recognized object has higher value.” Fast and frugal heuristics exploit the observed relationships between those objects in the environment for which inferences need to be made (Gigerenzer & Todd, 1999). For the recognition heuristic to be “ecologically rational” (i.e., works in the real world), there must be some correlation between a recognizable feature of an object (e.g., company brand) and a criterion (e.g., product or service quality) (Todd & Gigerenzer, 2003). The various fast and frugal heuristics identified by Gigerenzer and colleagues are part of a larger set of “tools” or psychological mechanisms that make up the human mind’s “adaptive toolbox” (Gigerenzer & Todd, 1999). The necessary tool is selected based on the task, the decision-maker’s knowledge, and external factors such as time pressure. The components of the tools themselves are simpler, more primitive and probably quite early-evolved building blocks which are the basic components from which new tools can be fashioned through learning (Gigerenzer & Todd, 1999, p. 33). Gigerenzer and Todd (2003) suggest that simple heuristics with their speed and robustness had an inherent advantage in the ancestral environment over more complex approaches and that the ability to use these simpler judgmental strategies “required the evolution of no more than a certain limited amount of cognitive capacity necessary to execute those heuristics” (Gigerenzer & Todd, 2003, p. 161).
It is important, given what will be said here in relation to intuition’s underlying cognitive architecture, to note that Gilovich and Griffin (2002) argued that the some of the prototypical heuristics studied by Gigerenzer and colleagues, such as “take the best,” are deliberately (i.e., consciously) chosen by the decision-taker to reduce the computational burden. In dual-process terms this renders them “System 2 [rational or analytical] heuristics” (Gilovich & Griffin, 2002, p. 17) in contrast both to the biased System 1 (intuitive) heuristics which were the focal point of Kahneman and Tversky’s work and the pattern-matching expert intuitions (i.e., System 2 analyzes “frozen” into System 1–like habitual responses) such as those studied by Chase and Simon and more latterly the NDM researchers.
Those who wrote about intuition in the 1980s and ’90s conceptualized the phenomenon in at least six different ways: as a paranormal power or sixth sense, as a personality trait, as an unconscious process, as a set of actions, as distilled experience, and as a residual category (Behling & Eckel, 1991). This diversity has spurred researchers to achieve some degree of consensus on what intuition is and underlines the importance of concept definition. A selection of definitions of intuition from the management, as opposed to psychology, literature (see Gilovich et al., 2002, for a psychological perspective) are offered here in chronlogical order: (1) mental processes “not capable of being expressed in words or as reasoning, which are only made known by a judgment, decision or action” (Barnard, 1938, p. 302); (2) “intuitions are analyses frozen into habit and the capacity for rapid response through recognition” (Simon, 1987, p. 63); (3) “[i]ntuition is knowledge gained without rational thought. It comes from some stratum of awareness just blow the conscious level and is slippery and elusive. Intuition comes with a feeling of ‘almost, but not quite knowing” (Rowan, 1989, p. 96); (4) “[a] feeling of knowing with certitude on the basis of inadequate information and without conscious awareness of rational thinking” (Shirley & Langan-Fox, 1996, p. 564); (5) “cognitive conclusion based upon the culmination of a decision maker’s previous experiences and emotional inputs” (Burke & Miller, 1999, p. 92); (6) “[i]ntuition is a capacity for attaining direct knowledge or understanding without the apparent intrusion of rational thought or logical inference” (Sadler-Smith & Shefy, 2004, p. 77); and (7) “[a] non-sequential information processing mode, which encompasses both cognitive and affective elements and results in direct knowing without any use of conscious reasoning” (Sinclair & Ashkanasy, 2005, p. 357). Notwithstanding the insights afforded to us by these definitions, it is appropriate to conclude this section with Dane and Pratt’s definition of intuition for two reasons—first, theirs has attained something approaching “consensual” status (Hodgkinson & Sadler-Smith, 2018), and second, as a definition, it is commensurable with the dual-process theoretic formulation preferred by many management intuition researchers: intuitions are “affectively-charged judgments that arise through rapid, non-conscious and holistic associations” (Dane & Pratt, 2007, p. 33).
A—perhaps the—fundamental question for management intuition research is “how is it [intuition] accomplished” in managerial work? (Simon, 1987, p. 59). One way in which management researchers have theorized their answer to this question is by drawing on insights from psychology to frame intuition in terms of a dual-process system of thinking and reasoning (Kahneman & Frederick, 2002; Sloman, 1996; Stanovich & West, 2000). Dual-process theory reflects a fundamental dichotomy in human thinking that has been referred to variously as automatic/controlled (Schneider & Shiffrin, 1977), associative/rule-based (Sloman, 1996; Smith & DeCoster, 2000), experiential/rational (Epstein, 1994; Epstein, Pacini, Denes-Raj, & Heier, 1996; Pacini & Epstein, 1999), implicit/explicit (Reber, 1993), intuitive/analytic (Hammond, Hamm, Grassia, & Pearson, 1987), and reflexive/reflective (Lieberman, 2007).
The neutral labels of “System 1” and “System 2” were coined by Stanovich (1999) for these processes in order designate their different properties but not to show any preference for any one of the many available theories. Evans and Stanovich (2013) are careful to distinguish between dual-processes, dual-systems, and dual-types as follows: (1) dual processes are two different types of processing, one faster and more automatic, the other slower and more controlled, evoked by cognitive tasks and contributing to observed behavior; (2) dual systems, or System 1 and System 2, are used to refer to fast-and-automatic and slow-and-deliberative processing, respectively; Evans and Stanovich prefer to avoid using the “system” label lest it lead to the misconception that the two types of processing are located in just two specific cognitive or neurological systems; (3) dual types are two qualitatively distinct types of processing, namely “Type 1 processes are (broadly) intuitive and Type 2 processes reflective” (Evans & Stanovich, 2013, p. 225).
The attributes of these two information-processing systems (referred to here as “intuitive” and “analytic”) may be summarized briefly as follows. The intuitive system (broadly equivalent to System 1, Type 1 processing, or the experiential system) is automatic, low-effort, experiential, heuristic, implicit, holistic, unconscious, domain-specific, slow-learning/fast-operating (for this reason it has been referred as the reflexive system, or X-system; see Lieberman, 2007), affective (Epstein et al., 1996), phylogenetically older (“old mind”; Evans & Stanovich, 2013), and referred to by some as “the intuitive mind” (Sadler-Smith, 2010). The analytical system (broadly equivalent to System 2, Type 2 processing, or the rational system) is controlled, high-effort, rational, systematic, explicit, conscious, domain-general, and fast-learning/slow-operating (for this reason it has been referred to as the reflective system, or C-system; see Lieberman, 2007), logical (Epstein et al., 1996), phylogenetically more recent (“new mind”; Evans & Stanovich, 2013), and referred to by some as “the analytical mind” (Sadler-Smith, 2010; see Table 1). Readers are referred to Epstein (1994), Evans (2008), Kahneman and Frederick (2002), Evans and Stanovich (2013), Lieberman (2007), and Stanovich and West (2000) for reviews of dual-process theories from psychological and neuroscientific perspectives and to Akinci and Sadler-Smith (2012), Hodgkinson, Langan-Fox, and Sadler-Smith (2008), Hodgkinson and Sadler-Smith (2018), and Sadler-Smith and Burke-Smalley (2015) for reviews from a business-management perspective.
Table 1. Summary of Dual-Process Models
Holistic; Automatic, Effortless; Affective; Associationistic connections; Behavior mediated by “vibes”; Encodes in concrete images, metaphors, narratives; Rapid processing; More resistant to change
Analytic; Intentional; Effortful; Rational; Logical connections; Behavior mediated by conscious appraisal; Encodes in abstract symbols, words, numbers; Slower processing; Less resistant to change
System 1 (Type 1 processes)
System 2 (Type 2 processes)
Unconscious; Implicit; Automatic; Low effort; Rapid; High capacity; Default; Holistic
Conscious; Explicit; Controlled; High effort; Slow; Low capacity; Inhibitory; Analytic
Associative; Domain specific; Contextualized; Parallel
Rule based; Domain general; Abstract; Sequential
Reflexive processing (X-system)
Reflective processing (C-system)
Parallel processing; Faster operating; Slower learning; Non-reflective consciousness; Spontaneous; Sensory; Unaffected by cognitive load; Phylogenetically older
Serial processing; Slower operating; Fast learning; Reflective consciousness; Intentional; Linguistic; Affected by cognitive load; Phylogenetically newer
Note: Epstein’s model is considered by Evans and Stanovich (2013) to be an example of a parallel-competitive formulation of dual-processing.
An important and yet unresolved issue relates to how conflict between the two systems is resolved in the processes of thinking, deciding, and problem solving in managerial work (Hodgkinson & Sadler-Smith, 2018). Two somewhat different proposals have been made, a “default intervention” model and a “parallel competitive” model, each reflecting different variants of the way in which System 1 and System 2 interact and interrelate (Evans, 2003). Parallel competitivism assumes that Type 1 and 2 processing proceed in parallel (Evans & Stanovich, 2013), whereas default interventionism assumes that “intuitive answers are prompted rapidly and with little effort when people are confronted by novel problems” (p. 237) and that Type 2 processing may or may not intervene subsequently on the involuntary intuitions that emanate from System 1 (p. 227). More specifically, in default interventionism the interaction between intuition and analysis has a sequential form in which Type 1 (intuitive) processes quickly and effortlessly generate a default response which may be “accepted or else intervened upon by Type 2 [analytical] reasoning” (Evans, 2010, p. 216). In parallel competitivism, on the other hand, conflict resolution occurs after heuristic and analytic processes have each proposed a response and “had their say.”
The processing mechanisms identified by the heuristics and biases researchers are typically default intervention: fast automatic default responses emanating from System 1 are typically not intervened upon by reflective System 2 reasoning processing since humans often act as “cognitive misers,” preferring to rely on easy-to-evaluate characteristics of a focal problem rather than harder-to-evaluate ones (Evans & Stanovich, 2013a, p. 237). Evans (2007) cites Epstein and colleagues’ “Cognitive-Experiential Self-Theory” as an example of a parallel competitive variant of dual-process theory. The assumptions of Epstein’s theory are that: (1) the experiential system and the rational system operate in parallel; (2) the two systems are bi-directionally interactive; (3) behavior is influenced by a combination of both systems; (4) behaviors are “experientially or rationally determined if they are determined primarily by one system or the other”; and (5) the relative contribution of either system is a function of the situation and the person (Epstein, 2008, p. 25).
It seems that the parallel competitive mode resonates strongly with how managers actually accomplish decision-making and problem solving in real-world settings. For example, Louis and Sutton (1991) used the metaphor of “shifting cognitive gears” to draw attention to the fact that in managerial work effective cognitive functioning involves the capacity to shift between automatic and deliberative processing and back again. Likewise, Hodgkinson and Clarke (2007) argue that managers who possess cognitive versatility are able to “attend to analytic detail and cut through that detail, as and when required” (p. 247, original emphasis). Both views resonate with the claim by Simon (1987) that the effective manager does not have the luxury of choosing between analytic and intuitive approaches to problems, but that being effective in managerial work involves having command of both modes and applying them as and when appropriate.
Default intervention and parallel competitive variants of dual-process theory are somewhat different and offer seemingly contradictory accounts of how intuitive processing operates. Researchers have yet to determine satisfactorily the way in which the dynamics between these two systems operate in the accomplishment of intuition in managerial work (Hodgkinson & Sadler-Smith, 2018). This question is important given that managerial intuition will only be understood imperfectly and partially if intuition is considered exclusively and in isolation from its contrastive, or complement, analysis.
Types of Intuition
Table 2 summarizes some of the main distinctions that intuition scholars and researchers have drawn between different types of intuition. The table offers brief descriptions of various types of intuition, and readers are referred to Dane and Pratt (2009) and Gore and Sadler-Smith (2011) for detailed expositions of three- and four-fold intuition typologies, respectively. In the following discussion, the focus will be on the four types of intuition that are most salient to management: expert intuition, creative intuition, moral intuition, and social intuition.
Table 2. Different Types of Intuition
Perceiving created beauty; conceiving uncreated beauty (Wild, 1938, p. 137)
Judgments based primarily on emotional reactions to decision situations (Pretz et al., 2014, p. 454)
Feelings that arise when knowledge is combined in novel ways (Dane & Pratt, 2009, p. 5); slow-to-form affectively-charged judgment occurring in advance of an insight that combines knowledge in novel ways based on divergent associations, and which orients behavior in a direction that may lead to a creative outcome (Gore & Sadler-Smith, 2011, p. 308)
Automatic acts of recognition due to pattern matching, also known as problem-solving intuition (Dane & Pratt, 2009, p. 5); domain-specific, expertise-based response to a tightly structured problem based on nonconscious processing of information, activated automatically, eliciting matching of complex patterns of multiple cues against previously acquired prototypes and scripts held in long-term memory (Gore & Sadler-Smith, 2011, p. 308)
Judgments based on a qualitatively non-analytical process, decisions made by integrating multiple, diverse cues into a whole that may or may not be explicit in nature (Pretz et al., 2014, p. 454)
Judgments based on automated inferences, decision-making processes that were once analytical but have become intuitive with practice (Pretz et al., 2014, p. 454)
Immediate solution of a problem un-preceded by any connected chain of reasoning (Wild, 1938, p. 137)
(1) problem solving: deciding about an alternative or about a direction; (2) moral: judging whether an action is good or evil; (3) aesthetic: judging something as beautiful or ugly (Dörfler & Ackermann, 2012, p. 15)
(1) problem solving: creating a solution which entails new knowledge; (2) moral: creating a new moral value; (3) aesthetic: creating something of beauty (Dörfler & Ackermann, 2012, p. 15)
A priori power of realizing the notions of right and wrong (Wild, 1938, p. 123); affective automatic reactions to issues that are viewed as having a moral/ethical component (Dane & Pratt, 2009); automatic, rapid, affect-based judgment made in response to an ethical dilemma, arrived at non-consciously, rationalized post hoc, and relatively impervious to disconfirmation (Gore & Sadler-Smith, 2011, p. 308)
Personal experience or knowledge of the divine or absolute experienced as a conviction or revelation of “unity” (Wild, 1938, p. 104)
Rapid and automatic evaluation of another person’s cognitive and/or affective state through the perception and nonconscious processing of verbal and/or nonverbal indicators (Gore & Sadler-Smith, 2011, p. 308)
It is also worth noting that several intuition researchers have distinguished between intuition, instinct, and insight and caution strongly against their being conflated (Hogarth, 2001; Sadler-Smith & Shefy, 2004). An instinct is an “inherent response tendency that occurs automatically” (Hogarth, 2001, p. 250), the term often being used metaphorically when referring to a manager’s judgment; however, it should not be confused with intuition (e.g., “going with one’s gut instinct”; Hogarth, 2001, p. 250). Insight is a sudden and unexpected thought that solves a perplexing problem and reflects the operation of subconscious processes (Hogarth, 2001; Wallas, 1926). Insight often occurs after a period of incubation (Wallas, 1926) and may be preceded by an “intimation” (Sadler-Smith, 2015). Dane and Pratt (2007) also distinguish between the process of intuiting and the outcome of this process, intuition.
Expert intuitions are analyses frozen into habit and the capacity for rapid response through recognition (Simon, 1987). Simon’s definition resonates strongly with later theories of intuition in which pattern recognition is given the pre-eminent role (e.g., Klein, 1998). Expert intuition (sometimes referred to as “problem-solving intuition”; Dane & Pratt, 2009) or intuitive expertise (Salas, Rosen, & DiazGranados, 2010), is an outcome of experienced participants’ rapid recognition of, and response to, situations characterized by familiar cues. Experts have access to large bodies of explicit and tacit knowledge assembled through learning (both explicit and implicit) and experience (Simon, 1987) stored in long-term memory as complex domain relevant schemas (CDRSs) (Dane & Pratt, 2007). Expert intuition is defined as a domain-specific, experience-based response to a tightly structured problem (i.e., where the nature of the associations is convergent) based on non-conscious processing of information, activated automatically, eliciting matching of complex patterns of multiple cues against previously acquired prototypes and scripts held in long-term memory (Gore & Sadler-Smith, 2011). Kahneman and Klein (2009) argued that the determination of whether an intuitive judgment can be trusted depends on the validities of both the task environment (high versus low) and participants’ learning (reflecting novice/expert differences).
High-validity environments are characterized by stable relationships between objectively identifiable cues and subsequent events, or between cues and the outcomes of possible actions. High-validity environments are to be found in domains such as medicine and firefighting, and intuition has been found to work in situations when participants are experienced and are thus able to exercise intuitive expertise (Klein, Calderwood, & Clinton-Cirocco, 2010). On the other hand, low-validity environments—for example predicting the future value of individual stocks or long-term political forecasting—are not suitable for intuitive judgment since these are not characterized by stable relationships between objectively identifiable cues and subsequent events or between cues and the outcomes of possible actions (Kahneman & Klein, 2009). Prolonged practice and timely and unequivocal feedback in high-validity environments (Hogarth, 2001) constitute the “kind” (as opposed to “wicked”) learning structure necessary for the development of intuitive expertise.
One potential source of confusion in intuition research, alluded to above, relates to the fact that insight and intuition are not the same (Hogarth, 2010). For a discussion of these relationships, see distinctions between intuitive judgments and intuitive insights mapped across the domains of problem solving, moral judgment, and aesthetics (Dörfler & Ackermann, 2012) and the re-conceptualization of Wallas’s classic four-stage model of the creative process (Sadler-Smith, 2015). Most intuition scholars consider intuition and insight to be closely related in the domain of creativity to the extent that a creative intuition may often segue to an explicit insight at the point of illumination or so-called “eureka moment.” Creative intuitions provide a visceral sense that can be interpreted subjectively by the intuitor as an intimation (literally “an announcement”) that a conjecture (such as a hypothesis, sketch, or plan) may work even though formal evaluation of its viability may be some way off (Gick & Lockhart, 1995; Sadler-Smith, 2015). Creative intuitions are defined as slow-to-form affectively charged signals that arise in advance of a subsequent insight that combines knowledge in novel ways based on divergent associations and orients behavior in a direction that may lead to a creative outcome (Gore & Sadler-Smith, 2011).
Creativity involves the generation of ideas that are both novel and valuable, and psychologists have long been concerned with the cognitive cognitions leading up to idea generation (e.g., Wallas, 1926). A five-stage process whereby this occurs (preparation, incubation, intimation, illumination, and verification) has been documented in Sadler-Smith’s (2015) close reading and critical re-appraisal of Wallas’s (1926) classic four-stage model of the creative process, The Art of Thought, in which he documented the work of a number of “creative geniuses.” Creative intuitions differ from expert (problem-solving) intuitions principally in that the former combine knowledge in novel ways through divergent associations, whereas the latter rely on convergent associations between an observed pattern and a prototype held in long-term memory (Dane & Pratt, 2009; Salas, Rosen, & DiazGranados, 2010). Creative intuitions, in which knowledge is combined in divergent, holistic, and novel ways, support scientific discovery, technical invention, business venturing, and artistic endeavor (Claxton, 2001; Dörfler & Ackermann, 2012; Dorfman, Shames, & Kihlstrom, 1996; Gore & Sadler-Smith, 2011; Miller & Ireland, 2005; Policastro, 1995; Sadler-Smith, 2010, 2015).
In contrast to traditional rationalist theories, the concepts of moral intuition and “intuitive ethics” draw on Haidt’s (2001) social intuitionist model (SIM) of moral judgment. Haidt’s model is based on the precept that individuals act like “intuitive moral attorneys” (Sonenshein, 2007) who search for confirmatory evidence for their initial intuitions (i.e., “gut feel” reactions to a moral dilemma). The model is “intuitionist” in that moral judgment is the result of quick, automatic intuitive evaluations rather than effortful moral deliberations (Haidt, 2001). The SIM also is consistent with a view of moral judgment as arising out of non-conscious pattern matching and accompanied by a high level of affective charge (i.e., moral intuitions present typically as powerful gut or visceral responses; Dane & Pratt, 2009). Reasoning is a post hoc attribution that can create illusions both of control and rationality (Sonenshein, 2007). Moral intuitions are fast, automatic, affect-based judgments made in response to an ethical dilemma; they are arrived at non-consciously, rationalized post hoc, and are relatively impervious to disconfirmation (Greene & Haidt, 2002; Haidt, 2001; Reynolds, 2006; Sonenshein, 2007).
Haidt’s SIM is social in that emphasizes the significance of social and cultural influences on moral judgment. In the model, individuals can be thought of as being “soft-wired,” rather than “hard-wired,” for moral intuitions, or akin to what Darwin referred to as a “moral sense” in The Descent of Man (1871) (see Sadler-Smith, 2012). Intuitions are learned gradually and implicitly by observation and imitation within the custom complexes of their socio-cultural setting whereby the latter provides a cultural front end for the generation of “gut feelings” (Haidt, 2001). Because they are socially influenced, moral intuitions are expressed differentially, within limits circumscribed by their underlying neural and biological foundations and processes (Mikhail, 2007; Sadler-Smith, 2012) and in accordance with the learning that takes place within the culture where they are enacted (Haidt, 2001). Dane and Pratt (2009), commenting in relation to the cultural interactionist aspects of intuitive moral judgment, noted that, as with broader cultural frames, it is entirely feasible that smaller cultures such as those of organizations can shape the moral and ethical codes of their members. This can not only lead to individuals internalizing the moral values of the culture in which they are located (or indeed its immorality and associated vices), it can also create tension when the values of an organization are contrary to an individual’s moral instincts (Sadler-Smith, 2012; Sonenshein, 2007). Senior leaders are especially important actors in this regard since the “tone from the top” can be a vital influence on the ethical climate of an organization (Sadler-Smith, 2019).
Social intuition is the ability to detect important attributes, motivations, and intentions of others; for example, people’s willingness as potential partners in valuable behaviors such as cooperation or mating could confer reproductive advantage and therefore be more likely to spread through a population (Almor, 2003, p. 105). This capacity to identify rapidly and automatically with the mental states, motives, feeling states, and intentions of others is depicted by Myers (2002, p. 33) thus: “When meeting a stranger in the forest, one had to instantly assess whether that person was friend or foe” with the corollary that individuals who could do so and act accordingly “were more likely to survive and leave descendants.”
Intuition researchers have distinguished between the valence of an intuitive response (positive or negative, indicating approach or avoid, respectively) and its level (from weak to strong) (Sadler-Smith, 2016). Whether another individual is judged as a “friend” (based on an intuitively evoked positively valenced appraisal, signaling attraction) or a “foe” (based on a negatively valenced appraisal, taken to signal avoidance) is a likely analogue for interpersonal interactions and social judgments that occur in occupational settings (such as selection interviews, negotiations, co-worker preferences, and group dynamics). Social intuitions are defined as rapid and automatic evaluation of another person’s cognitive and/or affective state through the perception and non-conscious processing of verbal and/or non-verbal indicators (Gore & Sadler-Smith, 2011). Nicholson (2000) considered our ability to make intuitive leaps as being necessary in order to both interpret the complexities of the natural world and deal with the complexities of our own kind in organizational settings. There are palpable similarities between the concept of social intuition and the emotional intelligence capability of “attuning to our feelings” (Goleman, Boyatzis, & McKee, 2013, p. 42).
Empirical evidence for the processes underlying “impressionistic” intuitive social judgments may be found in a program of work initiated by Ambady and colleagues in the early 1990s (see Ambady, 2010, p. 271). These researchers observed that ratings by complete strangers based on evaluative “thin slices” (video clips between 2 and 10 seconds in length) of teachers’ non-verbal behaviors predicted with high levels of accuracy the ratings of the same teachers by students who had interacted with them more substantially. These researchers also reported correlations between thin-slice judgments and real-life criterion variables (Ambady & Rosenthal, 1993). Other thin-slice research has found that the effectiveness of sales managers (as measured by supervisors’ evaluations and actual sales) can be assessed accurately using thin slices of the vocal channel of communication and that thin slices generally are more valuable for assessing interpersonal rather than non-interpersonal task-related skills (Ambady & Krabbenhoft, 2006). Other examples of such positive relationships include the following: surgeons rated as domineering on 20-second audio thin slices were more likely to have been sued for malpractice in the past (Ambady, La Plante, Nguen, Rosenthal, & Levinson, 2002); physical therapists who were rated on 20-second video thin slices as distancing themselves from patients (e.g., by not smiling or by looking away) were more likely to have clients whose physical and mental functioning showed long-term decline (Ambady, Koo, Rosenthal, & Winograd, 2002).
Related to this, Highhouse (2008) has commented on managers’ stubborn reliance on intuition in the selection process, despite the significant advances that have been made over the past half-century in the science of psychological assessment and in the tools and techniques of employee selection. The use of intuition in situations such as unstructured selection interviews (with the opportunity this creates for the biases inherent in intuitive judgment to come to the fore) opens the door to intuitive judgment as a source of bias, prejudice, and discrimination; for example, it is well established that we are intuitively drawn to people whom we perceive to be like us. As many have been keen to point out, in addition to being powerful, intuition is also potentially perilous (Myers, 2002).
This section begins with a critical appraisal of issues relating to the use of self-reports to identify individual differences in intuitive-versus-analytical processing (cognitive styles). It then proceeds to a discussion of a selection of alternative approaches and concludes with an examination of some recent developments in the fields of neurology and neuroscience which are pertinent to intuition research.
Measuring Intuition Using Self-Report
There are many—some might argue too many—self-report measures available for the assessment of individual differences in preferences for intuitive and analytical processing (variously described as “thinking style,” “cognitive style,” or “decision style”). The two measures considered here differ in an important respect and have been selected to make important theoretical and psychometric points. The first, the cognitive style index (CSI; Allinson & Hayes, 1996), is predicated on a “unitary” conceptualization (i.e., intuition and analysis are opposite ends of a bipolar continuum) of cognitive style. The second, the rational-experiential inventory (REI) (Epstein et al., 1996; Pacini & Epstein, 1999), is predicated on a “dual” conceptualization (i.e., intuition and analysis are independent, theoretically orthogonal constructs). The unitary and dual approaches are logically incommensurable—both cannot be correct. The issue of “unitary-versus-dual” is important for management intuition research given that the latter is more, and the former less, consistent with dual-process theorizations (Wang, Highhouse, Lake, Petersen, & Rada, 2017). This issue illustrates a theoretical tension in intuition research, at least as far as business and management studies is concerned (Hodgkinson & Sadler-Smith, 2003).
The Cognitive Style Index
The CSI (Allinson & Hayes, 1996) has been widely used in business and management as well as in educational research. It is predicated on a unitary model of cognitive styles in which analysis and intuition are positioned at opposite ends of a single bipolar dimension. The CSI is a 38-item self-report inventory that was developed initially from a pool of 129 items devised by its authors based on a thorough review of the cognitive styles literature (Hayes & Allinson, 1994). Allinson and Hayes (1996) used the term “intuition” to describe immediate judgment based on feeling and the adoption of a global perspective and the term “analysis” for judgment based on mental reasoning and a focus on detail. In terms of their behavioral manifestations, analytics prefer a structured approach to taking decisions and are more comfortable with problems that require a step-by-step approach; intuitives, on the other hand, prefer a more open-ended approach and work best with problems requiring an overall assessment and more random methods of exploration (Allinson & Hayes, 1996).
The CSI’s 38 items are scored on a trichotomous scale (true; uncertain; false) with a theoretical maximum score of 76 (the higher the score, the more analytical and less intuitive an individual’s style is and vice versa). There are 17 intuitive items (e.g., “I prefer chaotic action to orderly inaction”), which are negatively scored (true, 0; uncertain, 1; false, 2). The remaining 21 items (e.g., “I always pay attention to detail before reaching a conclusion”) are analytic and positively scored. In terms of its relationships with other well-established measures of intuition, Allinson and Hayes found statistically significant correlations between CSI scores and all four scales of the Myers-Briggs Type Indicator (MBTI); specifically, the CSI “correlated positively with the introversion and thinking poles, and negatively with the intuitive perception and perception poles, of the four MBTI scales” (1996, p. 27). It should be noted that the MBTI, like the CSI, is not predicated explicitly on a dual-process framework.
The CSI has been used widely in management and management education (for a review, see Coffield, Mosely, Hall, & Ecclestone, 2004). Hodgkinson and Sadler-Smith (2003) questioned the construct validity of the CSI and argued, from a dual-process standpoint, that its single-factor structure does stand up to empirical scrutiny. Hodgkinson and Sadler-Smith’s (2003) empirical evidence based on exploratory and confirmatory factor analyses on data from over 1,000 participants indicated that the CSI should be scored as two independent scales. Subsequent studies supported this interpretation (see Hodgkinson et al., 2009), although Armstrong and Qi (2016) challenged this view recently.
Rational Experiential Inventory
Epstein and colleagues’ cognitive-experiential self-theory (CEST) (Epstein, Pacini, Denes-Raj, & Heier, 1996; Pacini and Epstein, 1999) is a parallel-competitive variant of dual-processing (see Evans, 2007, for the distinction between parallel-competitive and default-intervention). As well as proposing two contrasting information processing modes—experiential (intuitive) and rational (analytic), broadly equivalent to Type 1 and Type 2 processing, respectively—CEST also accommodates the notion that there are individual differences in thinking (cognitive) style along two independent (rational and experiential) dimensions (in contrast to a single bipolar continuum as in the CSI). The REI is consistent with the basic tenets of dual-processing theory outlined above. For a full account of the theory, see Epstein (1994, 2008).
The REI exists in various forms. The original version comprised two unipolar measures, each with a five-point Likert scale response format: (1) “Need for Cognition” (NFC), a 19-item scale adapted from Cacioppo and Petty (1982), reflects “the extent to which individuals report that they enjoy and engage in, or dislike and avoid, cognitive activities” (Epstein et al., 1996, p. 394); (2) the “Faith in Intuition” (FI) scale, developed specifically for the REI, consists of 12 items designed to assess “confidence in one’s feelings and immediate impressions as a basis for decisions and actions” (Epstein et al., 1996, p. 394). Correlations between scores on the intuitive (“Faith in Intuition”) and analytical (“Need for Cognition”) scales of the long form of the REI have been consistently proven to be low and non-significant, indicating that rational (i.e., analytical) and experiential (i.e., intuitive) processing are independent dimensions of thinking style (Epstein et al., 1996).
An experiential (intuitive) thinking style is positively associated with esoteric beliefs, superstitious thinking, openness, positive thinking, naïve optimism, favorable interpersonal relationships, extraversion, agreeableness, favorable beliefs about the self and the world, sense of humor, creativity, social popularity, empathy, and aesthetic judgment and negatively associated with categorical thinking (Epstein, 2008, p. 28).
In a later refinement of CEST and the REI, Epstein and colleagues distinguished between ability and engagement with respect to both components (rational and experiential). Even though Hodgkinson, Sadler-Smith, Sinclair, and Ashkanasy (2009) found that the REI’s dimensionality was consistent with the dual-process view (two uncorrelated factors, −0.05 ≤ r ≤ 0.01), they did not find any compelling evidence for an ability/engagement distinction at the subscale level and also speculated on the existence of a common method factor. A shorter 10-item version of the REI exhibited low-scale intercorrelation (r = −0.09) (Epstein et al., 1996), and Akinci and Sadler-Smith (2012) suggested that this version possesses acceptable reliability and validity and its compactness suits it well to survey research in occupational and organizational settings.
Summary: Unitary Versus Dual Models of Cognitive Style
The ongoing debate regarding whether intuitive and analytical styles should be conceptualized and measured as a single (unitary) bipolar dimension or as two (dual) separate unipolar scales has reached a satisfactory resolution of late. A meta-analytical study by Wang, Highhouse, Lake, Peterson, and Rada (2017) reviewed the evidence relating to the inherent incompatibility of the unitary and dual models. From a database of 80 studies (N = 27,501), Wang et al.’s (2017) analyses showed that intuition and analysis are uncorrelated; their meta-analysis thereby supported the dual model. On the basis of theoretical arguments and an impressive weight of empirical evidence, it is incontrovertible that intuition and analysis should be viewed conceptually and psychometrically as independent constructs. Wang and colleagues concluded that their findings are consistent with previous research supporting a dual-process view (e.g., Hodgkinson & Sadler-Smith, 2003) and recommended that researchers should use measures such as the REI (Epstein et al., 1996) that were developed based on this “independence” perspective. They offered a strong cautionary note to the effects that adherence to the unitary view is “likely to lead to erroneous conclusions regarding the nature of cognitive style and its relation with general information processing” (Wang et al., 2017, p. 22).
Although there have been significant advances in intuition research in management in recent decades using traditional self-report measures (e.g., Khatri & Ng, 2000; Sadler-Smith, 2003), other methods have been used and should be further developed. It is important to look beyond self-report given that intuition presents researchers with a unique set of methodological challenges (Hodgkinson & Sadler-Smith, 2014). Self-report inventories do not lend themselves easily to accessing the process of intuiting, capturing intuitive episodes, inquiring into the subjective experience of intuition, or identifying the neural correlates of intuitive processes and outcomes. It is important also that researchers, in addition to studying the process of intuiting and its outcome (intuitive judgment), also use task-based measures in order to explore the relationships between intuition and outcomes in various domains, including ethical dilemmas, the use of expertise, creativity, and social judgment. Potential techniques to address these challenges include the adaptation of Flanagan’s critical incident technique (Akinci, 2014), the use of experience sampling (Csikszentmihalyi & Larson, 1987), and day reconstruction methods (Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004) to obtain first-hand accounts of intuitive episodes. A further approach, inclining toward phenomenology, is the linguistic technique of “de-nominalization” used by Sadler-Smith (2016). In this study of over 100 HR professionals, Sadler-Smith (2016) used the simple technique of not asking managers “What is intuition?” but instead asking them “What happens when you intuit?” in order to inquire into their subjective experiences of intuition in the workplace.
In a useful summary and overview, Sinclair (2014) categorized various approaches to researching intuition into several distinct areas: (1) cognitive systems and capabilities, e.g., expert intuition (Dreyfus, 2014); (2) the relationship between intuition and stress and emotions, e.g., stress and unconscious intuitive judgments (Grant & Langan-Fox, 2014); (3) quantitative approaches, including self-report, experimental studies and brain imaging, e.g., brain stimulation (Iannello, Colombo, & Antoinetti, 2014); (4) qualitative approaches to capturing the intuitive experience, e.g., concurrent protocol analysis (Baldacchino, Ucbasaran, Lockett, & Cabantous, 2014); (5) grounded theory approaches, e.g., the use of intuition by elite business leaders (Robson & Cooksey, 2014); and (6) using the novel idea of using intuition to research intuition (Dörfler & Eden, 2014).
In parallel with developments in organization neuroscience in general, the neuroscience of intuition has moved forward rapidly in recent years. This has been as a result of significant advances in research methods including neuro-imaging such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and related techniques (e.g., skin conductance responses). This section outlines two significant aspects of intuition research that have used neurological and neuroscientific approaches: the somatic marker hypothesis (SMH) and social cognitive neuroscience (SCN).
Somatic Marker Hypothesis
The somatic marker hypothesis is a proposed explanation for the biological mechanisms whereby affect is infused into the decision-making process under conditions of risk. Affect infusion is where affectively charged information exerts an influence on, and becomes incorporated into, cognitive and judgmental processes (Forgas, 2001). The SMH, developed by Damasio, Bechara, and colleagues in a series of groundbreaking studies based on the so-called Iowa Gambling Task (IGT) (see Damasio, 1999), provides a systems-level neuro-anatomical account for the mechanisms for how affect unconsciously influences risky decision-making. The SMH is based on the idea that decision-making is a process that is influenced by somatic (bodily) markers that arise in bio-regulatory processes, including those that express themselves in emotions and feelings and which act as an unconscious signaling mechanism in decision-making under risk and uncertainty (Bechara & Damasio, 2005, p. 336).
The origins of the SMH may be traced to experiments that compared the performance on a simulated high-risk gambling task (in the IGT) of normal participants with that of patients with damage to the ventro-medial prefrontal cortex (VMPC), a brain region implicated in the induction of emotions. Under simulated conditions of financial high risk, Bechara and Damasio found that normal participants (i.e., those without VMPC damage) began to choose advantageously before they were consciously aware which decision strategy worked best; moreover they generated anticipatory skin conductance responses (SCRs) before they exercised a risky choice and before they became consciously aware of the decision-making approach they were about to adopt. Patients with VMPC damage, on the other hand, continued to choose disadvantageously, even after they realized the correct strategy, and they failed to generate anticipatory SCRs in spite of the risk involved in the task (Bechara, Tranel, Damasio, & Damasio, 1997).
As far as intuition and decision-making under risk is concerned, the SMH posits that damage to the VMPC region can result in the impoverishment of decision-making apparatus to a dramatic degree under conditions of risk. One effect can be the decision-maker becoming stuck in an “analysis paralysis,” unable to decide between two essentially identical alternatives. Clinical issues aside, the SMH offers a compelling explanation for how bodily markers exert an important influence on higher-order cognitive activities such as planning and decision-making (Damasio, 1999, p. 302).
Social Cognitive Neuroscience
During the 1970s the so-called “split-brain” research of Roger Sperry and colleagues studied the differential activities between the two hemispheres of the brain (initially in animal studies and then latterly in studies of human patients suffering from epilepsy). This work was taken up enthusiastically by some management scholars to account for differences in intuitive and analytical processing in managerial work (e.g., Mintzberg’s famous 1976 Harvard Business Review article “Planning on the Left-Side, Managing on the Right”). The suggestion in much of this work was that managerial intuition emanates from the “right brain” (for a critique of this approach, see Hines, 1987) whereas planning and analysis are “left-brain” processes. It is now generally agreed that this was an overinterpretation and at most represents a convenient metaphor for two kinds of information processing (Hodgkinson et al., 2008). What is becoming clear is that intuitive decision-making emanates from a network of brain systems that do not correspond to a simple left–right split (Campitelli, Gobet, Head, Buckley, & Parker, 2007; Satpute & Lieberman, 2006; Saxe, Carey, & Kanwisher, 2004; Volz & Von Cramon, 2006).
Social cognitive neuroscientists such as Lieberman and colleagues have identified dual-processing distinctions (referred to as the reflexive and reflective systems, abbreviated to X-system and C-system) underlain not by intuitive or analytical “hot spots” in the brain but by an interacting network of neural structures and subsystems (for a review, see Lieberman, 2007). For example, reflexive (intuitive) processing is not correlated to the “right brain” as many have claimed, rather a complex interacting network of systems are involved including the ventro-medial prefrontal cortex (VMPC) and amygdala. A series of experiments by Lieberman, Jarcho, and Satpute (2004) that used fMRI to compare intuition-based and non–intuition-based judgment found a differential pattern of brain activations across the brain. In non–intuition-based judgments (where participants had low experience), activations were associated with the “reflective” neural system, or C-system, which is distributed across the lateral prefrontal cortex, posterior parietal cortex, and hippocampus. In intuition-based judgments (where participants had high experience), activations were associated with the reflexive system, or X-system, distributed across the ventro-medial pre-frontal cortex (VMPC—the same region implicated in the SMH studies), basal ganglia, and amygdala (Lieberman et al., 2004).
Intuition was once considered a vague and hard-to-define phenomenon, in the domains of the paranormal and transcendentalism (Behling & Eckel, 1991), beyond scientific investigation and not something that could be trained, developed, or cultivated. Developments in intuition research in management and other fields, such as educational studies (see, e.g., Claxton, 2001), have challenged these claims: intuition can be defined and theorized, intuition is open to scientific methods of inquiry, and training, developing, and cultivating managers’ intuition is eminently achievable. Sadler-Smith and Shefy (2010) summarized the strategy for doing this as a three-pronged approach: (1) developing intuitive expertise, that is knowledge of one’s domain of professional practice; (2) developing one’s knowledge and understanding of intuition, that is knowing what intuition is, how it arises, and the conditions under which intuition is more and less likely to work; and (3) developing intra-subjective intuitive self-awareness, that is, meta-cognitive knowledge of one’s own “intuitive mind” (Sadler-Smith, 2010).
Sadler-Smith and Shefy’s strategies are aimed at cultivating “intuitive intelligence” and can be approached in three complementary ways. First, the view of intuition as a product of a significant period of learning (both implicit and explicit), coaching, and feedback indicates that expert intuition is only learnable and that “intuitive muscle power” cannot be acquired in any other manner than through intense and extended practice (Hogarth, 2001; Kahneman & Klein, 2009; Sadler-Smith & Shefy, 2004). Second, developing business students’ and managers’ awareness of the topic of intuition requires the study of intuition to be incorporated into the management training and development curriculum, both in business school and in in-company programs in a way as to complement and counterbalance the current emphasis given to rationality (Burke & Sadler-Smith, 2006, 2011). Third, given that intuitions are transitory and in some respects ineffable by nature, individuals are more likely to be able to capture their intuitions “in flight” if they are in an attentive and mindful state (Dane, 2011). In addressing this proposal, Sadler-Smith and Shefy (2007) developed, tested, and evaluated several approaches for developing intuitive awareness with practicing managers. An important implication from Sadler-Smith and Shefy’s (2007) study was that any attempt at developing intuitive awareness should avoid being overly prescriptive but instead should adopt a flexible and personalized approach whereby individual students or managers have the scope to understand, explore, and develop their own intuitive intelligence once they have been given the knowledge and tools and techniques for doing so.
Finally, it is noteworthy that work in this area has had a wider developmental impact on the general public and on government policy. For example, Ariely (2009), Kahneman (2011), and Thaler and Sunstein (2009) have popularized heuristics and the System 1/System 2 distinction and stressed their practical implications, as in Kahenman’s best-selling book Thinking, Fast and Slow. Thaler won the 2017 Nobel Prize in Economics, and Sunstein served as director of the Office of Information and Regulatory Affairs, and together they have spurred the creation of so-called “nudge units” worldwide.
Discussion and Commentary on the Literature
An extensive intuition literature has accumulated in management research since the early 1970s. This body of knowledge evolved mainly out behavioral decision theory (e.g., Kahneman & Tversky, 1973) and bounded rationality (e.g., Chase & Simon, 1973; Simon, 1987). In the late 1980s and 1990s the influential concept of intuitive expertise manifested most notably in the naturalistic decision-making research (NDM) of Klein and colleagues (Klein, Calderwood, & Clinton-Cirocco, 1988, 2010; Zsambok & Klein, 1997). At a higher level of abstraction, dual-process models have offered management researchers an integrated and coherent theoretical account of the nature and the role of intuition in thinking, reasoning, problem solving, and decision-making (Evans & Stanovich, 2013; Kahneman & Frederick, 2002). Research in neuroscience shows that intuitive processing involves a complex interplay of cognitive, affective, and somatic mechanisms that have identifiable neural correlates (Bechara & Damasio, 2005; Lieberman, 2007). This stream of work also reveals the idea of intuition being located in the “right brain” and analysis in the “left” as being little more than a convenient metaphor for two fundamentally different types of thought (Hines, 1987; Hodgkinson et al., 2008). A parallel stream of intuition research has focused on fast-and-frugal heuristics where actors use simple rules (e.g., recognition) based on principles of ecological rationality to deliberately limit information search (Gigerenzer & Goldstein, 1996, p. 650).
Intuition research in management is at an important juncture. Although the phenomenon of intuition has proven to be of perennial interest and relevance to the practice of management and important strides forward have been made in intuition research, new challenges are faced. For example, intuition research in management thus far has, quite understandably and unsurprisingly, focused largely on intuition. However, intuition in management will only be imperfectly understood if intuitive (System 1) processes are considered in isolation from their analytical (System 2) counterparts. We need to better understand how intuition and analysis are jointly complicit in the accomplishment of decision-making in managerial work. It should also be noted that the dual-process view of intuition that is central to much of intuition theory and research in management is not uncontested (e.g., Dreyfus, 2014; Kruglanski & Gigerenzer, 2011).
The challenges faced may also invite researchers to think differently about intuition. For example, an important opportunity has presented itself to cross-fertilize, or at a minimum communicate between, the psychological and behavioral approach that has tended to dominate management intuition research with alternative ontologies and epistemologies (Jeanes & Sadler-Smith, 2018). Recent empirical and theoretical work in organization studies has drawn on paradox theory to better understand the tensions between intuition and analysis and the ways in which managers resolve the dynamics of their interrelationships and interrelatedness (Calabretta, Gemser, & Wijnberg, 2017; Keller & Sadler-Smith, 2018). Likewise, process philosophy and process organization studies (Helin, Hernes, Hjorth, & Holt, 2014; Hernes, 2008, 2014; Langley & Tsoukas, 2017) are underexplored and potentially productive avenues of inquiry not only for intuition research but also for decision-making research generally. By way of illustration of this latter point, Henri Bergson, writing in An Introduction to Metaphysics over a century ago, demarcated two profoundly separate ways of knowing: “intellectively” and “intuitively” (Bergson, 1913; Chia, 1997). Intellective ways of knowing imply that we move around an object of inquiry, reliant on the symbols by which we express ourselves; this kind of knowledge stops at the “relative.” Intuitive ways of knowing imply that we “enter” the object; it depends neither on a point of view nor on any symbol and entails the possibility for experientially attaining the “absolute.” In management and organization studies intuition is of central importance in understanding—to paraphrase Herbert Simon—not only how decision-making is accomplished in management (Simon, 1987, p. 63) but also how it evolves in an ongoing process of managing.
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