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An organization’s experts, whether executives at the strategic level or workers at the tactical level, have vast amounts of knowledge and years of experience that allow them to interpret the daily deluge of data they encounter. Their knowledge of their organization and its operation, its direction, and situations that arise from day to day is transformed into mental models that are indispensable for understanding the world. Mental models organize knowledge in both simple and robust ways “in a world awash with information of staggering complexity” (Vandenbosh & Higgins, 1996, p. 200). These mental models are the structures that allow experts to process new information and to make better decisions than novices who have less well-developed mental models.
Mental models are vital for understanding, organizing, and responding to our world (Bartlett, 1932). However mental models are also limiting as they channel people into selecting and using data that confirms and then reinforces existing models (Festinger, 1957). Experts are constrained by their mental models that act as blinders to anything “outside the box” (Pennington, 1987). They have trouble surfacing and examining the tacit, unconscious knowledge and experience needed to make creative decisions before a crisis hits.
While mental models channel and limit conscious thought, the mind is also unconsciously collecting, interpreting, and analyzing data from a vast array of diverse sources. This “conglomerate of perceptual, affective, and cognitive processes” form the “rapid, unconscious, nonlinear information processes” known as intuition (Lussier, 2016, p. 709). Intuition is an essential management trait that can bypass the limitations of executives’ mental models and help them jump to creative solutions to novel problems.
Intuition is often dismissed as an “irrational mystical force” (Bergson, 1911). These intuitive hunches arise seemingly out of nowhere. They are cognitive leaps that seem right although there is no actual data to back them up. In fact, intuition is the mind’s form of data mining. It is most often defined as “the act of cognition without rational inference.” It is a way of learning that takes place beyond consciousness where a decision-maker acquires knowledge but is unable to identify the source of this knowledge (Malewska, 2015, p. 98). Experts spend a lifetime acquiring data (knowledge) that intuition sometimes forces to the surface as “hunches.” While the usual definition of a hunch is “subjective, generalized, unreasoned and therefore unreliable” (Lerner, 2006, p. 407), we use the definition derived from cognitive science that a hunch is the product of intuition, which is “a capacity for attaining direct knowledge or understanding without the apparent intrusion of logical inference” (Sadler-Smith & Shefy, 2004, p. 77).
Experts’ deep knowledge is tapped, and mental models are challenged, during times of crisis. When they need to make time-critical, highly complex decisions, experts often turn to intuition to come up with “out of the box” solutions (Dane & Pratt, 2007). Even in today’s world of big data, analytics, and artificial intelligence, experts are encouraged to make more decisions using intuition to help train deep learning algorithms (Kahn, 2019). There is a considerable and growing body of research that explores intuition and its role in decision making in turbulent environments (Hodgkinson & Sadler-Smith, 2018; Khatri & Ng, 2000), and is seen as a key element that differentiates between successful top executives and lower level managers (Agor, 1986). However, virtually all this research examines the use of hunches and intuition in connection with decision making under pressure. This paper explores surfacing latent hunches: those hunches that are not being used for immediate decision making but can be exploited for building models useful for data mining, data analytics, and for training artificial intelligence algorithms.