Leader Thinking Skills

Capacities for Contemporary Leadership

Michael D. Mumford, Cory A. Higgs, Michael D. Mumford, Cory A. Higgs

• Publisher: Routledge
• Year: 2019
• Print ISBN: 9781138284319
• eBook ISBN: 9781351979474
• Topic: Psychology
• Subtopic: History & Theory in Psychology
• Index: Psychology

This book examines the various thinking skills that leaders may need to find success in contemporary organizations and institutions, covering a wide array of skills that are held to be important by key leadership scholars.

Bridging theory and practice, chapters summarize major findings with respect to a particular ability, knowledge, or skill, providing theoretical frameworks for understanding how these contribute to leader emergence and performance, and considering implications for leader selection, assessment, and development. The text appraises the existing research on the critical cognitive capabilities that underlie leader problem-solving and implications for the assessment and development of leadership potential in real-world settings. The role of creative thinking skills on leader performance is also addressed, bearing on the importance of processes such as problem definition and idea generation, but also using constraints to potentially stimulate creative thought.

With contributions from some of the most eminent scholars working in the field of leadership, this book will be in invaluable resource to academics, researchers, graduate students, and professionals interested in leadership and leader skills, I/O psychology, and business management.

1
Intelligence and Leadership

John Antonakis, Dean Keith Simonton, and Jonathan Wai

During the 2016 United States presidential campaign, the leading contender for the Republican nomination, Donald Trump, boasted of his superior intelligence, and after his election he more explicitly claimed to have a genius-level IQ. In contrast, the 43rd president of the United States, George W. Bush, was often viewed as exhibiting only an inferior intellect. Indeed, an internet hoax at the time estimated his IQ at a below-average 91, very different from the 156 score that supposedly belonged to Trump (Simonton, 2018). What these two events illustrate is that intelligence is widely deemed by the public to be relevant to presidential performance (see Cohen, 2018, for empirical evidence).

In fact, it should be obvious to us why intelligence should matter in some capacity for success in any leadership position, political or otherwise. Leaders must be able to “join the dots”, that is, learn from information in their environment; they must be attuned to inferring from multimodal data signals—whether emotional, economic, behavioral—abstract from them, identify condition action links, and decide a course of action that increases the likelihood of success (Antonakis, 2011). They should be able to think fast, too, which is a characteristic of intelligent individuals (Baker, Vernon, & Ho, 1991; Sheppard & Vernon, 2008). The capacity to learn is key to modern notions of intelligence (Gottfredson, 1997a), and intelligence is vital for leadership success. Yet, has research discovered what seems to be so obvious?

It turns out that the relation between intelligence and leadership is not particularly obvious. Instead, the association, though empirically present, varies and is rather more subtle than expected. This subtlety is demonstrated in the two main research traditions that have tackled this question: the psychometric and the historiometric. In this chapter we discuss these issues and others to make the case that intelligence is a key determinant of leader success. We bring to the fore issues concerning measurement, the criterion predicted, the importance of context, and methodological issues that should be considered by applied researchers interested in the topic of intelligence.

Psychometric Intelligence

Psychometric research applies quantitative techniques to assess how individuals vary on abilities, traits, preferences, or other factors. These techniques date back to the very beginning of scientific psychology (e.g., Cattell, 1890; Galton, 1883). For our purposes, naturally, the most relevant methods are those associated with the measurement of intelligence as a trait. We adopt the definition that traits “are individual characteristics that (a) are measurable, (b) vary across individuals, (c) exhibit temporal and situational stability, and (d) predict attitudes, decisions, or behaviours and consequently outcomes” (Antonakis, 2011, p. 268). After discussing the general research literature on psychometric intelligence, we turn to the specific question about how the resulting scores are associated with leadership.

Research on General Intelligence

The idea of intelligence is not by any means a new concept. Throughout history, philosophers have discussed human faculties. Aristotle, for example, discussed the “five wits”: imagination, fantasy, memory, reason, and the “common sense” (Ritchie, 2015). In The Republic, Plato wrote extensively about what qualities, including intelligence, would be required for effective leadership (Antonakis, 2011). The Chinese developed some of what might be considered the first criteria for measuring intelligence and hence assess the ability to “infer and predict from given information” (Higgins & Xiang, 2009, p. 258).

The first true intelligence tests were developed by the psychologist Alfred Binet from the study of students who struggled intellectually. With Theodore Simon, Binet developed such tests as a way to identify students with learning disabilities. Lewis Terman further developed these tests to identify students with learning gifts, and during the First World War, Robert Yerkes designed tests that could be administered efficiently to large groups of potential military recruits. These were the first group intelligence tests; they formed the basis for many intelligence and standardized tests that we see in use today across various educational and occupational settings (for more comprehensive reviews of the history of intelligence and the development of intelligence tests, see Haier, 2016; Hunt, 2011; Ritchie, 2015).

Some of the subsequent discussion surrounding intelligence has to do with how to best define or operationalize it. Though there are many verbal definitions of intelligence, Gottfredson’s (1997a) definition is often cited by intelligence researchers:

Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience. It is not merely book-learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings—“catching on”, “making sense” of things, or “figuring out” what to do.

(p. 13)

Thus, overall, intelligence has to do with the ability to learn; however, others have questioned the validity of such a definition and prefer the “rate with which learning occurs” (Carroll, 1997, p. 43). Important to note, too, is that speed of information processing is correlated with g (general intelligence) and in particular measures of fluid intelligence at r = .35 (Sheppard & Vernon, 2008); this correlation is probably explained by common genetic causes (Baker et al., 1991).

We do not wish to belabor readers with definitional issues, especially because there is no consensus on how intelligence should be defined (Carroll, 1997). In any case, scientific advancement occurs largely through the quantification or measurement of key variables in order to determine genuine relationships between those variables. Therefore, researchers have proposed that quantitative definitions of intelligence can advance the field beyond verbal arguments ( Jensen, 1998; cf. Lubinski, 2004). Carroll (1993) synthesized the structure of human cognitive abilities through a comprehensive factor analysis of mental test data at the time, showing that general intelligence, or g, is important, in addition to specific abilities such as math and verbal abilities, along with a number of other abilities. Thus, though there are a number of cognitive abilities beyond a general factor, including specific abilities that have incremental prediction for outcomes (e.g., see Lubinski, 2004), g remains the largest source of predictive variance among a host of outcomes; therefore, g is the focus of the remainder of this brief review (later, we briefly discuss the utility of alternative notions of intelligence).

In what ways can general intelligence be measured? Spearman (1927) noted that the specific content of a test may not be particularly important, because g is important for performance on mental tests generally. Nearly any challenging mental test that includes a wide variety of items will measure g to some extent (Chabris, 2007; Ree & Earles, 1991), and, in fact, g is measured to some degree in tests that were purposefully designed to measure a diverse array of achievements and abilities ( Johnson, te Nijenhuis, & Bouchard, 2008). Even measures commonly thought of by many researchers and practitioners as achievement or aptitude tests, such as the American College Test (ACT) or Scholastic Assessment Test (SAT), have been shown to actually measure g to a large degree (Frey & Detterman, 2004; Koenig, Frey, & Detterman, 2008), though of course such tests also reflect developed abilities. Researchers have also demonstrated that academic achievement g and cognitive g are essentially the same (Kaufman, Reynolds, Liu, Kaufman, & McGrew, 2012), which means that achievement and cognitive tests essentially both measure the same g. Some recent advances in the measurement of intelligence include chronometric research, the use of elementary cognitive tasks or reaction times to “clock the mind” (Beaujean, 2005; Jensen, 2006). Intelligence can be measured with better reliability than any other individual differences variable ( Jensen, 1998).

The Nature and Nurture of Intelligence

Intelligence, like most other traits, is due in part both to nature and nurture. The nurture aspect regarding the development of intelligence, however, must account for the nature aspect. Thus, research on intelligence from the field of behavioral genetics is important because it may ultimately help us understand which aspects of the environment are important to bring out each individual’s intelligence and broader array of cognitive abilities to the fullest. There are two approaches being used: first, investigating the extent to which genes contribute to intelligence differences (known as quantitative genetics, often using twins and siblings), and, second, investigating which are the specific genes in the DNA that, when they differ among people, cause intelligence differences (known as molecular genetics). To date, an enormous amount of evidence has accumulated surrounding the heritability of intelligence using twin samples; however, there is now headway in the molecular genetics area when studying intelligence (for comprehensive reviews, see Plomin & Deary, 2014; Plomin & von Stumm, 2018). Suffice it to say, the evidence has accumulated such that intelligence is in part due to nature as well as nurture.

The Significant Correlates of Intelligence

General intelligence, or g, has been called a “rosetta stone” or a crucial key given its broad links to many phenomena of interest in psychology, social science, and society ( Jensen, 2006). Large amounts of data spanning the last century have indicated links between intelligence and numerous outcomes of consequence, including educational achievement, occupational success, income, mortality, and others (e.g., for reviews, see Gottfredson, 2003b; Haier, 2016; Hunt, 2011; Jensen, 1998; Ritchie, 2015). To offer some illustrations, higher intelligence has been linked to positive health-related habits, such as exercising more, eating better, and smoking less (e.g., see Gottfredson, 2003b; Gottfredson & Deary, 2004). People with higher intelligence are less likely to have medical issues such as heart disease, obesity, or hypertension, and this is found for both physical and mental health (Wraw, Deary, Gale, & Der, 2015). Higher intelligence has also been linked to lower mortality or death risk (Batty, Gale, Tynelius, Deary, & Rasmussen, 2009). Higher intelligence has even been linked to higher creativity (Mosing, Pedersen, Madison, & Ullén, 2014; Nusbaum & Silvia, 2011; Wai, Lubinski, & Benbow, 2005). Intelligence has also been linked to political preferences and religion, among an array of other outcomes (for an extensive list of correlations between g and many outcomes, see Jensen, 1998; Strenze, 2015).

However, for the purposes of this review, two sets of outcomes are most relevant. First, general intelligence has been linked to many educational outcomes. A very large and representative sample from the United Kingdom (Deary, Strand, Smith, & Fernandes, 2007) examined the scores of over 13,000 students at age 11 and correlated those scores with educational achievement at age 16. The researchers found that the correlation between g and the overall exam score was r = .81. This correlation is very high, especially in relation to what is typically found in social science, and indicates the importance of general intelligence for educational achievement.

In the United States, the entire range of SAT scores (which, as noted earlier, also measure g) were discovered to be linearly related to college grade point average (GPA; Cullen, Hardison, & Sackett, 2004). In addition, SAT scores are associated with a wide variety of outcomes, including graduate school performance (Berry & Sackett, 2009). Kuncel, Hezlett, and Ones (2004) conducted a major meta-analysis illustrating that g as tapped by the Miller Analogies Test (MAT) predicted a range of graduate student academic criteria. General intelligence predicted GPA, comprehensive examination scores, time to degree completion and attainment, faculty ratings, and research productivity.

Studies of intellectually precocious youths who have participated in a 7th-grade US talent search—where students complete the SAT before age 13—provide...

Table of contents

• Cover
• Half Title
• Title
• Copyright
• Contents
• Note on Contributors
• Leader Thinking Skills
• 1 Intelligence and Leadership
• 2 Leadership and Information Processing: A Dynamic System, Dual-Processing Perspective
• 3 Uncertainty and Problem Solving: The Role of Leader Information-Gathering Strategies
• 4 Are Satisfied Employees Productive or Productive Employees Satisfied? How Leaders Think About and Apply Causal Information
• 5 Thinking About Causes: How Leaders Identify the Critical Variables to Act On
• 6 Leaders’ Shifts in Attention During an Organizational Crisis: Longitudinal Evidence of Responses to a Crisis Within a Top Management Team
• 7 Creative Problem Solving: Processes, Strategies, and Considerations for Leaders
• 8 Seeing the Future Through the Past: Forecasting Skill as a Basis for Leader Performance
• 9 Leader Decision Making Capacity: An Information Processing Perspective
• 10 Making Sense of Leaders Making Sense
• 11 Leaders, Teams, and Their Mental Models
• 12 Leader Social Acuity
• 13 Leadership and Monitoring Skills
• 14 Wisdom, Foolishness, and Toxicity in Leadership: How Does One Know Which Is Which?
• Index