Strategic Intelligence - Wiley Online Library

Strategic Management Journal Strat. Mgmt. J., 38: 2390–2423 (2017) Published online EarlyView 11 September 2017 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/smj.2660 Received 26 June 2016; Final revision received 21 March 2017

Strategic Intelligence: The Cognitive Capability to Anticipate Competitor Behavior Sheen S. Levine,1* Mark Bernard,1 and Rosemarie Nagel2 1 Organizations, Strategy and International Management, Naveen Jindal School of Management, University of Texas at Dallas, Dallas, Texas 2 Catalan Institution for Research and Advanced Studies (ICREA), Universitat Pompeu Fabra, and Barcelona Graduate School of Economics, Barcelona, Spain

Research summary: Pursuing sources of entrepreneurial and competitive advantage, researchers have been exploring cognition. We examine how cognitive capabilities affect competitive performance, drawing on two constructs rooted in psychology and economics. A familiar one is analytic skill, the ability to solve abstract problems. To that, we add strategic intelligence — the ability to anticipate competitors’ behavior and preempt it. Using incentivized experiments, we measure the constructs in participants, then let them compete for cash in a highly competitive market. Although the market is designed to eliminate any advantages, whether from market structure or strategic resources, some profit much more than others. We trace performance differences to heterogeneity in analytic skill and strategic intelligence, and show how the two fuel superior performance, even against tough competition. Managerial summary: Why do some entrepreneurs outperform others? How can companies succeed against tough competition? Certainly, some benefit from unique resources, such as patents, and others can winnow competition, as through mergers. But some have entered highly competitive markets, lacking obvious resources, yet managed to achieve impressive success: think Under Armour, Wal-Mart or Home Depot. Here we test how advantage can stem from managerial cognition. We measure two kinds of cognitive skill in market participants, and then let them vie for cash in intensely competitive markets. Some end up with far more profit than others. Tracing the root of high performance, we find it is predicted by a combination of analytic skills, the ability to solve abstract problems, and strategic intelligence—ability to anticipate competitors’ behavior and preempt it. Copyright © 2017 John Wiley & Sons, Ltd.

“Professional investment may be likened to those newspaper competitions in which the competitors have to pick out the six prettiest faces from a hundred photographs, the prize being awarded to the competitor whose choice most nearly corresponds to the average preferences of the competitors as a whole; so that each competitor has to pick, not those

faces which he himself finds prettiest, but those which he thinks likeliest to catch the fancy of the other competitors, all of whom are looking at the problem from the same point of view … we devote our intelligences to anticipating what average opinion expects the average opinion to be. John Maynard Keynes (1936, p. 156)”

Keywords: behavioral strategy; game theory; experiment; market; competition; performance

“ ”. “Know the enemy and know yourself; in a hundred battles you will never be in peril”. Sun Tzu (1963, p. 84)

*Correspondence to: Sheen S. Levine, 800 W Campbell Road, Richardson, TX 75202. E-mail: [email protected]

Copyright © 2017 John Wiley & Sons, Ltd.

Strategic Intelligence Playing college football, Kevin Plank was bothered by sweaty undershirts. Under their protective pads, he and the other players wore cotton T-shirts. During practice or play, these quickly became sweaty, heavy, and uncomfortable. Noticing the velvety microfiber used in women’s undergarments, he imagined a lighter-weight, faster drying T-shirt made from the synthetic material. He browsed fabrics at a tailor shop, spent $500 buying some, and began sewing prototypes, handing them to friends for testing. After graduation, he started a business, which he named Under Armour. In his grandmother’s basement, he spread and cut fabric, made sales calls, and packaged shipments. Friends from his college team, who went on to become professional football players, spread the word about the startup. After a few months, a university team placed a big order, asking for 350 shirts. At the time, he had only 60. He delivered in installments, and soon more orders followed. The major makers of athletic wear, such as Adidas and Nike, seemed oblivious to the startup. After all, they had multimillion-dollar marketing budgets, and Plank could afford only a fraction of that. Yet the company grew quickly, its products adopted by college players, professionals, and fans. In 2003, Under Armour brought a revenue of $110 million. In 2012, he was the youngest on the Forbes list of billionaires, with an estimated wealth of $1.35 billion. In 2016, the company’s market capitalization was more than $17 billion (Bruke, 2012; John, 2016; Palmisano, 2009; Plank, 2003). Where does competitive advantage come from? This question, which lies at the heart of strategy, has been receiving answers of different kinds: Some focus on the industry, others on firm resources and capabilities (Hoskisson, Hitt, Wan, & Yiu, 1999). One school of thought suggests that success comes from consciously choosing which industry to enter, and then shaping a position within it. By erecting barriers to entry, maintaining an oligopoly, increasing customer switching costs, or otherwise curbing competition, firms can reap supernormal profits (Caves & Porter, 1977; McGahan & Porter, 1997; M. E. Porter, 1980, 1981). Another school seeks competitive advantage inside the firm, speaking of unique resources and capabilities that thrust a firm to outperform its rivals (Barney, 1991; Wernerfelt, 1984). But neither offers an obvious explanation for, let alone a clear prediction of, the ascendance of Under Armour: The startup entered a market characterized Copyright © 2017 John Wiley & Sons, Ltd.

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by low barriers to entry and dominated by large competitors, whose apparent resources included, at the very least, well-established brand names. Under Armour could not match their spending on marketing or research and development (R&D). Plank may have pioneered using microfiber in athletic wear, but the fabric was widely available and not patentable. Imitation was easy. Reviewing everything the fledgling company possessed—without succumbing to tautological reasoning (Priem & Butler, 2001)—it is difficult to see what could have foretold Under Armour’s success. Using microfiber may be valuable, but it is hardly rare, inimitable, or nonsubstitutable, thus failing the tests for sustainable competitive advantage (Barney, 1991). Under Armour is not a single case: Walmart in retail, Home Depot and Menards in home improvement, KB Home in construction, and Enterprise in car rental all entered highly competitive markets with no obvious paths to strategic resources. Their products may have been clever (KB Home slashed house prices by eliminating basements), but technology was common, methods visible in use, and intellectual property unprotected. Even if these innovators had some initial advantages, they operated in a competitive environment that eased imitation and thus thwarted appropriation of innovations (Jacobides, Knudsen, & Augier, 2006; Levin et al., 1987; Teece, 1986). Yet these innovators, and many like them, have managed to become multibillion-dollar behemoths. Now, of course, they have advantages from market dominance and difficult-to-imitate resources, such as brand name, but what could account for their initial success, in crowded markets and with no obvious resources? We can only guess: Perhaps Under Armour’s success had to do with the managerial cognition of Plank, the founder. Perhaps he grasped that his gigantic competitors were too preoccupied to notice the emerging niche in athletic wear. Perhaps other companies have likewise eluded competitors. But how, exactly? In recent years, scholars have rekindled the interest in the role of individuals, an interest that dates back to Simon (1947), Penrose (1959), and Cyert, March, and their associates (K. J. Cohen & Cyert, 1961, 1965; Cyert, Feigenbaum, & March, 1959; Cyert & March, 1963). Building on their work, Nelson and Winter (1982) offered a robust alternative to a central behavioral assumption—that of calculative, rational decision-makers, an assumption inherited from neoclassical economics. Because Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

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individual rationality is bounded, they reasoned, an organizational advantage comes from routines, patterns of interaction between people. Their perspective gave rise to the notion of dynamic capabilities, defined as routines that govern other routines (at least in part, see Eisenhardt & Martin, 2000; Peteraf, Di Stefano, & Verona, 2013; Teece, Pisano, & Shuen, 1997; Zollo & Winter, 2002). And, more recently, the strategic role of individuals—and their cognitions—has returned to center stage. Some scholars have been theorizing on the cognitive capabilities that can build a competitive advantage, whereas others have been calling for more empirical evidence for the role of individuals and their patterns of interaction (Abell, Felin, & Foss, 2008; Coff & Kryscynski, 2011; Felin & Foss, 2005; Foss, 2011; Gavetti, 2005; Hodgkinson & Healey, 2011; Teece, 2007; Winter, 2013). Also, more empirical evidence came to illuminate decision making in strategically important situations (e.g., Gary, Wood, & Pillinger, 2012; Lovallo, Clarke, & Camerer, 2012; Mitchell, Shepherd, & Sharfman, 2011; Park, Westphal, & Stern, 2011). Contributing to the growing interest in behavioral strategy and micro-foundations, here we examine the role of mental processes. These, as Helfat and Peteraf (2015) lament, are perhaps the least understood area of managerial cognition. And, as they recognize, “heterogeneity of … cognitive capabilities may produce heterogeneity of dynamic managerial capabilities among top executives, which may contribute to differential performance of organizations under conditions of change” (831). Research Question We aim to understand how cognitive skills, such as those of an entrepreneur or a CEO, affect performance in an exemplary strategic environment—a highly competitive market. Taking advantage of recent empirical evidence in experimental economics, psychology, and cognitive science, we hypothesized that performance in competition is related to two kinds of cognitive skills, one focused internally and the other—externally. To understand the role they can play in performance, we created instruments to measure the two and built in a laboratory a highly competitive market. We individually measured the two sets of cognitive skills in participants and then let them compete for profits, paid in cash, in a market designed Copyright © 2017 John Wiley & Sons, Ltd.

to eliminate most possibilities of performance differences, including advantages from ex-ante or ex-post limits to competition, or from limits to resource mobility (Peteraf, 1993). To suppress sources of advantage that are unrelated to cognition, such as market structure or firm resources, our design features the five characteristics of a competitive market per economic theory: Competitors are atomistic, products are homogenous, information is complete and public, everybody has equal access to technology and resources, and entry is free (Cabral, 2000, pp. 85–86). In such situations, theory stipulates, price should be equal to marginal cost (p = MC), leaving no room for profits.1 Yet, even in this highly competitive setting, where theory predicts no competitive advantage and identical (normal) profits, some competitors do much better than others. We trace the performance differences to measurable ex-ante differences in the two cognitive skills: analytic skill, or the ability to reason through abstract problems, and strategic intelligence, or the ability to anticipate competitors’ behavior—and preempt it. We find that the two are uncorrelated and each considerably boosts competitive performance. In an organizational setting, analytic skills could relate to technical know-how, domain-specific expertise, and problem-solving skills. In contrast, strategic intelligence is outward gazing: focused externally on understanding and anticipating others, especially competitors (as opposed to bettering the internal workings of the firm). Per the taxonomy of Adner and Helfat (2003), both skills stem from cognition.2 And both fit the broad definition of managerial cognitive capability (Helfat & Peteraf, 2015), yet they differ in how the capability is applied and what the consequences are. We find that analytic skills and strategic intelligence benefit performance independently, but also

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Much of the scholarship in strategic management has been seeking ways to weaken perfect competition, for instance, by erecting barriers to entry or by developing capabilities to improve quality or reduce cost, thereby lessening homogeneity (e.g., Barney, 1986; Porter, 1980; Winter, 1995). Diminish any of the five characteristics, and a firm can move closer to a monopoly position, giving it more control over prices and quality (Tirole, 1988). 2 As in prior literature (e.g., Helfat & Peteraf, 2015), we follow the common definition of cognition in psychology: “processes of knowing, including attending, remembering, and reasoning; also, the content of the processes, such as concepts or memories” (American Psychological Association, 2009) and “the mental activities involved in acquiring and processing information” (Colman, 2006). Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

Strategic Intelligence interact: Those who are high in analytic skill benefit most from strategic intelligence, suggesting complementarity. Intuitively, to succeed in competition, one must understand the nature of one’s strategic environment, yet it is even better to anticipate competitors’ moves and preempt them. In the context we studied—a highly competitive market—analytic skills matter more for buyers, probably in recognizing an opportune acquisition. Strategic intelligence matters more for sellers, possibly allowing them to price with an oversized premium. There are multiple contributions here: One, we provide evidence on how cognitive capabilities affect performance in competition. The findings apply broadly: Whenever an individual can affect firm performance. This is true in entrepreneurial settings, where a founder may yield much decision-making power, but this is also true with top managers, organizational constraints aside. Two, we show that such cognitive capabilities allow one to outdo competitors even in a highly competitive market, one that ostensibly thwarts any form of competitive advantage. Three, we compare the performance effects of two kinds of dynamic managerial capabilities: Analytic skills, which stem from human capital and have been studied much, and strategic intelligence, which stems from social cognition and has been linked to strategy less frequently. Four, by pinpointing the source of advantage conveyed by strategic intelligence, we can suggest the circumstance in which it would matter the most. Five, we address a noted lacuna in the study of cognition and performance: Thanks to our method we can simultaneously examine cognition, an individual, microlevel construct—and performance in competition, a collective, macrolevel construct. This approach allows us to span insights from decision-making and organizational behavior, where the focus is on individual decisions, often in noncompetitive settings, and studies in economics and strategy, where the focus is on performance in competition, on how outcomes are decided when encountering rivals.3 3

An example of focus on individual decision making is in studies of prospect theory, which investigates how a person chooses among risky propositions. In contrast, performance in competition is not decided by any single actor, an individual or a firm. For example, a firm can decide to accumulate resources or cut prices, but it cannot decide how a competitor would react to these decisions, and what would be the ultimate outcome of actions and reactions. Thus, performance is a collectivist (Abell et al., 2008, p. 490) or a macro outcome, as the term is used in economics


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By empirically identifying a role for cognitive capabilities in competitive performance, we answer the call to identify the microfoundations of strategy and the sources of macro outcomes, such as performance in competition (Felin & Foss, 2005; Gavetti, 2005; Helfat & Martin, 2015; Helfat & Peteraf, 2015; Teece, 2007). By identifying underlying psychological foundations of these cognitive capabilities, we contribute to the emerging behavioral theory of strategy (Gavetti, 2012; Greve, 2013; Hodgkinson & Healey, 2011; Powell, Lovallo, & Fox, 2011). And by tracking the influence of cognitive reflection, stemming from the cognitive faculty often called System 2 (Kahneman, 2003; Stanovich & West, 2000), we contribute evidence to the discussion on mindful and mindless behavior (Laureiro-Martínez, 2014; Levinthal & Rerup, 2006) and of habit and contemplation (Winter, 2013). Finally, students of entrepreneurship have been engulfed in a philosophical debate about opportunities: are they discovered, created, or perhaps enacted? (Kirzner, 1997; Eckhardt & Shane, 2003; Schumpeter, 1934 [1912]; Alvarez & Barney, 2007; Ramoglou & Tsang, 2016). We do not purport to settle the debate, but we may be able to inform it with some empirical evidence.

How Individual Skills May Affect Performance Individual Capabilities and Ccompetitive Advantage That individual capabilities matter to economic performance is no novelty. Early on, Marshall (1947, p. 685) observed the rising fortunes flowing to individuals of “extraordinary ability” and Rosen (1981) formalized it in a theory of superstar performers. Bringing organizations to the discussion, Penrose (1959) spoke of the interaction between managerial and firm resources. Cyert, March, and their associates were early students of organizational and managerial decision making, highlighting the interaction between individual cognition and organizational decisions (K. J. Cohen & Cyert, 1961, 1965). They opined on the importance of experimental research (Cyert et al., 1959, p. 94) and included experiments in their celebrated Behavioral Theory of the Firm (Cyert & March, 1963, pp. 52–98). (Schelling, 1978), organizational theory (Cyert & March, 1992 [1963], pp. 85–92), and elsewhere. Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

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Later, Huff (1982) and Prahalad and Bettis (1986) spoke of managerial schemas that guide cognitive processes, leading to firm-level outcomes. Zajac and Bazerman (1991) theorized how the varying capacity of individuals for competitive decision making, particularly their blind spots, can explain industry phenomena such as excess market entry and botched acquisitions. Mahoney (1995) was early to link the resource-based view, organizational capabilities, and cognitive managerial capabilities, such as those expressed in grasping the dynamics of an industry. Managerial Capabilities Managerial capabilities are germane here, because—per definition—they can be dynamic, hierarchical, and reside in (or emanate from) individuals. First, whereas some capabilities are functional, such as engineering or marketing know-how, others are dynamic: They promote economically important change, especially the modification of lower-level, functional routines and capabilities (Helfat & Winter, 2011; Winter, 2003). Because they can modify other capabilities, dynamic capabilities can be hierarchical. Following the example of the differential calculus, Collis (1994) imagined dynamic capabilities of the second order, third order, continued ad infinitum, because “the capability that wins tomorrow is the capability to develop the capability to develop the capability that innovates faster (or better)” (148). Winter (2003, p. 991) noted that the value of higher-level capabilities depends on understanding “the ‘level of the game’ at which strategic competition effectively occurs.” This understanding is enhanced by strategic intelligence, as detailed below. Second, dynamic capabilities, such as the ability to identify and exploit opportunities (Teece et al., 1997) or succeed in strategic decision making (Eisenhardt & Zbaracki, 1992), can emerge at the organizational level, as in routines—but they can also be managerial or entrepreneurial, emanating from individual cognition (Augier & Teece, 2009; Rindova, 1999; Teece, 2007; Zajac & Bazerman, 1991). In recent years, cognitive managerial capabilities have been drawing much scholarly interest. Scholars have been theorizing on such capabilities (e.g., Gavetti, 2012; Helfat & Martin, 2015; Helfat & Peteraf, 2015), aided by studies, mostly archival or qualitative, that documented the relationship between managerial cognition and performance. Copyright © 2017 John Wiley & Sons, Ltd.

Discussing dynamic managerial capabilities, Adner and Helfat (2003, p. 1013) postulated three core underpinnings: cognition, human capital, and social capital. Working in this tradition, researchers have provided evidence that individual capabilities underlie many of the intangible resources that contribute to competitive advantage, such as employee know-how, product and firm reputation, culture, innovation, and social networks (Hall, 1993). Having the right people may bring a competitive advantage (Campbell, Coff, & Kryscynski, 2012; Coff, 1997; Ganco, Ziedonis, & Agarwal, 2015; Mollick, 2012), and that advantage may suffer as they depart (e.g., Aime, Johnson, Ridge, & Hill, 2010; Campbell, Ganco, Franco, & Agarwal, 2012). Not surprisingly, the skills of some people matter greatly: Top managers can play a central role in creating and sustaining a competitive advantage (e.g., Castanias & Helfat, 1991; Collins & Clark, 2003; Finkelstein, Hambrick, & Cannella, 2008; A. Goldfarb & Xiao, 2011; A. Goldfarb & Yang, 2009; Hambrick & Quigley, 2014; Weng & Lin, 2014). Owners and executives affect performance and firm value, positively and negatively (e.g., Feldman & Montgomery, 2015; Park et al., 2011; Patel & Cooper, 2014; Sauerwald, Lin, & Peng, 2016), and their effect may have increased in recent years (Quigley & Hambrick, 2015). Managerial capabilities may matter even more in nascent firms where individual entrepreneurs may wield greater influence, such as in the opening example of Under Armour. Researchers have documented links between individual cognition and the fundamental entrepreneurial decision of entering a market (Cain, Moore, & Haran, 2015; C. Camerer & Lovallo, 1999) or existing in it (A. Goldfarb & Xiao, 2016). Also, decisions made early in the life of an organization can have the power to direct and limit future action through path dependency and imprinting (Kraatz & Zajac, 1996; Stinchcombe, 1965). Competitive Advantage From Gazing Inward—and Outward Researchers have been hypothesizing how managerial cognition can bring a competitive advantage. One way is by looking inward: If firms are composed of distinct but related activities (Gavetti, 2005; Postrel, 2002), then superior performance can come from better managing and synchronizing these activities (see a review in Helfat & Martin, 2015). This managerial skill was early noted Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

Strategic Intelligence by Penrose (1959, p. 5) and Alchian and Demsetz (1972), among others. It has garnered much theoretical and empirical attention (e.g., Hansen, Perry, & Reese, 2004; Holcomb, Holmes, & Connelly, 2009; Kor & Mahoney, 2005; Kunc & Morecroft, 2010; Laamanen & Wallin, 2009; Mahoney, 1995). The inward-looking perspective is prevalent in the capabilities literature, and some scholars attempted to complement it by adding an outward-looking component (e.g., Lavie, 2006). But here we focus on the gaze outward: How individuals grasp (or not) their competitors’ intentions. We define and measure an outward-looking skill, strategic intelligence, that can bring an advantage. We find that it varies between individuals. We also show that it predicts actual performance in a highly competitive environment: a market featuring the theoretical characteristics of perfect competition. That such a skill exists has been a persistent notion among theoreticians. Again, it was Penrose (1959, p. 42) who spoke of a manager who can envision a superior “subjective productive opportunity” for the firm. In the output decision model of Cyert et al. (1959, p. 85), the first step is “forecasting a competitor’s behavior.” Later, March and Olsen (1976) recognized that some individual beliefs about the environment may be more accurate than others, and Barney (1986) noted how some firms may excel in recognizing sources of potential value in markets. Sensing threats and seizing opportunities, two strategic capacities enabled by the outward gaze are two of three enumerated in Teece’s (2007, p. 1319) definition of dynamic capabilities. Gavetti, Levinthal, and Rivkin (2005) modeled differences in the capability of decision-makers to analogize from one industry to another; and Gavetti (2005, 2012) further theorized on individual differences in the ability to grasp a competitive environment. Empirical research supports the notion that decision-makers differ in their capabilities of modeling a competitive environment. The gaze outward is evident in studies of managerial mental models, as in noticing when a competitor acts, when dynamics of the market change (e.g., Cattani, Porac, & Thomas, 2017; Greve, 1998; Marcel, Barr, & Duhaime, 2011; Porac, Thomas, & Baden-Fuller, 1989; Reger & Palmer, 1996), or when the regulatory or technological environment shifts (e.g., Barr, Stimpert, & Huff, 1992; Bingham & Kahl, 2013; Eggers & Kaplan, 2009; S. Kaplan, 2008; S. N. Kaplan, Murray, & Henderson, 2003; Tripsas & Gavetti, 2000). Copyright © 2017 John Wiley & Sons, Ltd.

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These outward-looking capabilities have been often assessed through corporate documents, such as corporate letters to shareholders and annual reports, through surveys and interviews of managers, and through the analysis of text in trade publications. Such expressions of managerial cognitions are important and informative. Here we complement them with direct observation of individual behavior, which we can link to revealed performance in competition. In doing so, we hope to strengthen the “bridge connecting the macro and micro levels” (Eggers & Kaplan, 2013, p. 295), by contributing direct evidence about the micro mechanisms underlying macro outcomes, such as performance heterogeneity.

Analytic Skills, Strategic Intelligence, and Performance How Analytic Skills Affect Performance Analytic skills, when deployed as inward-gazing cognitive skills, seem likely to bring better decisions when optimizing, coordinating, and managing cooperative efforts, as opposed to interacting strategically, vis-à-vis competitors. As noted, if firms are composed of distinct but related activities (Gavetti, 2005; Postrel, 2002), then superior performance can come from better managing and synchronizing these activities (see a review in Helfat & Martin, 2015). The best evidence comes from research that correlated managers’ analytic skills and firm performance. It showed that better performance is associated with proxies for higher analytic skills, such as higher SAT scores, academic studies in selective institutions, training in business or economics, and post-graduate education (Chevalier & Ellison, 1999; A. Goldfarb & Xiao, 2011; S. N. Kaplan, Klebanov, & Sorensen, 2012). This correlational evidence leads us to expect: Hypothesis 1: Better analytic skills improve performance. How Strategic Intelligence Affects Performance In “The Purloined Letter,” Edgar Allan Poe (1845) offered a memorable illustration of strategic intelligence, as defined here. He tells how a police commissioner fails to recover a stolen letter. The commissioner stumbles not due to negligence—his Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

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detective work is meticulous—but because he misjudges the thief’s sophistication. He perceives the thief to be as sophisticated as he is, and thus searches for the letter in the same inconspicuous places where he would have hidden a stolen good: behind the wallpaper, in chair rungs, inside book covers. But the thief had predicted the commissioner’s state of mind, so he foils him by hiding the letter in plain view, hanging from a mantelpiece. The commissioner never looks there. The police commissioner may be an exacting detective, but the thief outsmarts him by grasping his thought process, predicting where he would look for the letter, and then placing it elsewhere. The thief’s success stems from anticipating how the commissioner would behave. Doing that requires understanding how other people perceive, feel, and think—a set of skills known collectively as social cognition (Frith & Singer, 2008; Gallese, Keysers, & Rizzolatti, 2004; Nagel, 1995). It is related to theory of mind, mentalizing, and mind-reading (Engel, Woolley, Jing, Chabris, & Malone, 2014; Gallagher & Frith, 2003; Gallese & Goldman, 1998; Singer & Fehr, 2005; Woolley, Chabris, Pentland, Hashmi, & Malone, 2010). John Maynard Keynes (1936, p. 156) spoke of such skills when commenting that success in the stock market consists not of anticipating fundamentals or firm performance, but of “anticipating what average opinion expects the average opinion to be.” Researchers in social cognition focus on the cognitive representations that people use to understand other people and their behavior (Fiske & Taylor, 2013). Like cognitive psychologists, they describe the mental processes that link stimuli to responses, such as attention, memory, and inference, but they apply these processes to social settings, when people interact with others.4 Social cognition is a broad capability: “It underpins our ability to deceive, cooperate and empathize” (Gallagher & Frith, 2003, p. 77). It affects a multitude of contexts: emotional and cognitive (Hein & Singer, 2008); cooperative (Davis, 1983) and competitive. Social cognition—how people think about people—seems like a natural interest for strategy scholars, yet few have investigated how it affects performance in competition. Here, we define 4 Therefore, when management strategy scholars speak of cognition, as in attention or mental schemes, they typically draw on social cognition, which investigates how people think about people.


strategic intelligence as the application of social cognition to gain strategic advantage: the ability to anticipate competitors’ behavior—and preempt it. The higher the strategic intelligence, the better one can anticipate rival behavior, infer their likely strategies, and preempt them. Theoreticians have suggested that such skills have developed in the course of human evolution (Mohlin, 2012; Robalino & Robson, 2016).5 We hypothesize that analytic skills improve performance, but they may not suffice when the optimal strategy depends on the actions of others. There, success hinges on accurate mental representations of others, such as rivals, because the ultimate value of any action depends on their choices and responses. From games such as rock-paper-scissors and chess to decisions about employee management, market entry, R&D investments, and geographical locations, many choices are strategic in the strict sense of the word—the outcome hinges not only on the characteristics of the decision, such as the presence of cognitive biases, but also on the simultaneous choices or subsequent reactions of others (for empirical and theoretical elaboration, see Crawford & Iriberri, 2007; Erev & Rapoport, 1998; Hampton, Bossaerts, & O’Doherty, 2008) (for applications to strategy, see Brandenburger & Stuart, 2007; Gans & Ryall, 2017). The notion that others’ actions matter for strategic outcomes is fundamental to economic thinking (Keynes, 1936; Schumpeter, 1934 [1912]) and present in many strategic models: “Anticipating and studying competitor moves is a key aspect of strategy practice and theory” (Stea, Linder, & Foss, 2015, p. 283). For instance, when a decision-maker contemplates entry to a new market, she knows that simultaneous entry by competitors will lower prices and harm profitability (Lippman & Rumelt, 1982). It is also true, for instance, when deciding on R&D investment. There, the value of the investment 5

Strategic intelligence is what Bhatt and Camerer (2005) and Coricelli and Nagel (2009) call strategic IQ (but differs from the way Wells (2011) uses it). It resembles competitor acumen (Tsai, K-H, & Chen, 2011) and trader intuition (Bruguier, Quartz, & Bossaerts, 2010). The notion of perspective taking in social psychology has a broader meaning, like that of social cognition, for it “allows an individual to anticipate the behavior and reactions of others” (Davis, 1983, p. 115). It can facilitate smoother and more rewarding relationships (Davis, 1983) and better negotiation outcomes (Galinsky, Maddux, Gilin, & White, 2008). But, as we show here, it also plays a role in competitive behavior (and sometimes detrimentally, e.g., Ding, Wellman, Wang, Fu, & Lee, 2015; Epley, Caruso, & Bazerman, 2006; Pierce, Kilduff, Galinsky, & Sivanathan, 2013). Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

Strategic Intelligence increases with the likelihood of an innovative outcome, but decreases on the likelihood that competitors will quickly imitate this innovation (Knott, Posen, & Wu, 2009; Nelson & Winter, 1978). Without strategic thinking, technological excellence may not matter. An example demonstrates how strategic intelligence turns accurate social cognition to an economic advantage: A favorable location, such as a corner shop, can confer competitive advantage (M. E. Porter, 1980, p. 11). Yet, whether a given location is favorable depends not only on the resources intrinsically tied to the location, such as ambience or convenience, but also on the proximity to competitor outlets, current and future. So, an optimal decision depends both on the intrinsic resources of the location, assessed using analytic skills, and the behavior of competitors, preempted using strategic intelligence. Consider this illustration of how one’s performance is partially dependent on the actions and reactions of competitors (Ho, Camerer, & Weigelt, 1998, p. 947): Picture a thin country 1000 miles long, running north and south, like Chile. Several natural attractions are located at the northern tip of the country. Suppose each of n resort developers plans to locate a resort somewhere on the country’s coast (and all spots are equally attractive). After all the resort locations are chosen, an airport will be built to serve tourists, at the average of all the locations including the natural attractions. Suppose most tourists visit all the resorts equally often, except for lazy tourists who visit only the resort closest to the airport; so the developer who locates closest to the airport gets a fixed bonus of extra visitors. Where should the developer locate to be nearest to the airport? Such situations are strategic in the most essential sense of the word: Performance depends on the actions (and reactions) of competitors. And, as in all strategic situations, articulation is often lacking, competitor skill and intention are hard to decipher, reactions are uncertain, and so are outcomes. As Levinthal (2011, p. 1522) remarked, such situations are the hallmark of strategic management. For this scenario, which represents a dominancesolvable game, the game-theoretic solution is this: all the developers should locate exactly where the natural attractions are. This “does not depend Copyright © 2017 John Wiley & Sons, Ltd.

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on the fraction of lazy tourists or the number of developers (as long as there is more than one)” (p. 947). Intuitively, all developers would have ended up selecting an identical location because they would have repeatedly moved closer to the average of all locations, until they are all located at zero, exactly at the northern tip of the country. There, the average is zero and thus nonimprovable. This answer, which represents the Nash equilibrium, is true if all developers were to think through the strategic problem thoroughly and alike, attempting to predict their competitors’ behavior, and undergoing multiple iterations of action and reactions. But in real life a successful action requires not only the analytic skills of evoking multiple dynamic scenarios—it also requires an accurate grasp of others’ cognitive capabilities. If others behave irrationally (or bounded rationally), for instance, deciding on a location randomly, neglecting to consider competitive reactions, or myopically stopping after an iteration or two—then a simpler response may be better than a sophisticated one. As Keynes (1936, p. 156) observed, the correct answer sometimes depends on accurate representation of others’ cognition—including their cognitive limitations. Those who perform well in such situations do so because they can predict competitor behavior, so analytic skills may not suffice in such situations.

The Guessing Game, a Measure of Strategic Intelligence How do people compete in such a strategic situation, where winning requires anticipating what competitors will do, rationally or not, thoughtfully or myopically? To investigate this, Nagel (1995) designed an experiment now known as the Guessing Game or the Keynesian Beauty Contest. In its canonical version, the experiment asks each participant in a crowd to “guess a number, between zero and hundred, that is closest to two thirds the average of all numbers submitted.” Performing well depends on analytic skills: You must be able to calculate a hypothetical average and find two-thirds of that value. But the strategic nature of this game is also obvious: Grasping the hypothetical average depends on your ability to anticipate what the others will choose and respond to, all while realizing that they are simultaneously attempting to predict what you (and everyone else) will choose. Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

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A reader familiar with game theory will recognize that, like the decision about resort location, this appears to be a dominance solvable game. If a player is rational and believes her rivals are rational and everybody believes everyone else is rational (and so on), then she may engage in the iterated elimination of weakly dominated strategies.6 To begin with, a rational player has no good reason to choose any number greater than 2/3 × 100 = 66 2/3 because any such number is weakly dominated by 66 2/3. Regardless of what a player believes about the others, a number greater than 66 2/3 cannot be the winning number. Now, if the player believes that her rivals are also rational, and therefore no one will choose any number above 66 2/3, she should replace 100 with 66 2/3, following the same reasoning. Then, she will conclude that she should never guess any number above 2/3 × 66 2/3 = (2/3)2 × 100 = 44 4/9. Iterating on this logic, and observing that (2/3) n × 100 converges to 0 as n increases, we obtain that all players should choose the number 0. In game theoretic terms, this is game’s unique Nash equilibrium (for a proof, see Nagel, 1995, p. 1314). But when Nagel examined actual behavior in an experiment, she revealed a surprise: the strategy for winning is never the Nash equilibrium. She found that people differ in the depth of their strategic reasoning. Here, it means that they differ in the number they assume will be average (𝜇) and the number of iterations, or steps, they perform. In studies involving thousands of participants, researchers documented three common guesses (Bosch-Domènech, Montalvo, Nagel, & Satorra, 2002, p. 1691): One is 33.333, which is two-thirds of 50. Those who pick this number behave as if all other competitors are naïve and simply submit a random number, so 𝜇 = 50. Empirically, this is rarely true. Another popular guess is zero, the Nash equilibrium, which implies that one believes that her rivals are completely rational, assumes everyone else is rational, and so on, perhaps game theory mavens. This belief is also rarely true. The winning guess in this game 6 A more conservative approach would be to use iterative elimination of strictly dominated strategies, which is implied by common knowledge of rationality. While iterated elimination of weakly dominated strategies does not logically follow from common knowledge of rationality without some auxiliary assumption, it is intuitive and more suitable to illustrate the mechanics of the Keynesian Beauty Contest, and hence we employ it here. The original Beauty Contest can also be solved using iterated elimination of strictly dominated strategies, but that argument involves players randomizing between choices (technically speaking, using mixed strategies).


is neither of these. In student experiments the winning number is typically between 20 and 30, thus betweemn 1 or 2 steps of 50, while in newspaper experiments it is between 13 and 17 around 3 steps of reasoning. Simply put, most people do not grasp how most people think. Ultimately, the best performers, those who guess closest to two-thirds of the average of all numbers submitted, do not assume their rivals are naïve. But they also do not assume that most people engage in iterated elimination of weakly dominated strategies to reach zero. Empirically, most people do not engage in deep strategic thinking but are not completely naïve either (e.g., Duffy & Nagel, 1997; Gill & Prowse, 2016), and the best performers understand that. In game theoretic terms, the best performers identify the iterated best response: the strategy that produces the most favorable outcome for a player, considering other players’ strategies (Gibbons, 1992, pp. 33–49). The best performers are seemingly able to grasp the others’ mindset, anticipating their actions. Much like the thief who outsmarted the police commissioner in Poe’s story, the best performers in the Guessing Game are those who gauge how deeply their competitors think. They anticipate that some will behave as if everyone else is naïve and some will assume that everyone else is completely rational, and so the resulting average is somewhere in between, so the best performers pick two-thirds of that number, and win. Scholars have repeatedly documented this pattern in many variations of the Guessing Game, in the laboratory and the field, among laypersons and experts (even when participants are all economists), and in long sessions involving thousands of competitors (Bosch-Domènech et al., 2002; Brañas-Garza, García-Muño, & Hernán, 2012; Burnham, Cesarini, Johannesson, Lichtenstein, & Wallace, 2009; Coricelli & Nagel, 2009; Crawford, Costa-Gomes, & Iriberri, 2013; Ho & Su, 2013; Ho et al., 1998; Nagel, Bühren and Frank, in press). The value of strategizing depends on how the competition fits with theoretical ideas in strategic management. For instance, Winter’s (2003, p. 991) postulation that higher-level capabilities are not always beneficial: their value depends on “the ‘level of the game’ at which strategic competition effectively occurs.” It is possible, then, that a high performer is not the one who merely possesses high analytic skills. It is the one who possesses those skills but also the strategic intelligence to Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

Strategic Intelligence understand that most people do not. A high performer realizes that competitors vary in their strategic thinking. Because not all people are equally deep thinkers, reasoning too deeply— attributing too much shrewdness to a competitor, performing too many strategic iterations—could be detrimental. In the resort example, locating at the northern tip may be a mistake. There, the deepest thinker would not necessarily be the best performer. The best performers would be those who possess high analytic skills but also understand how their competitors behave. A different way to think about strategic intelligence is to consider its absence as a “behavioral market failure” (Gavetti, 2012). A competitor with a higher strategic intelligence understands that not all competitors are sophisticated. They might miss opportunities. Therefore, even if a market is perfectly competitive and firms are identical—where competitive advantage cannot stem from ex-ante or ex-post limits to competition, or from limits to resource mobility (Peteraf, 1993)—there may still be opportunities, neglected by others. High strategic intelligence will push a competitor to seek those opportunities and exploit them for better performance. Some competitors will perform better than others, thanks to superior strategic intelligence. This possibility is buttressed by the finding that, when a competitor underestimates the performance of others, his performance suffers (Dosi & Lovallo, 1997). For instance, a skilled home cook may compare himself to the general population, find correctly that he is better than the vast majority, and thus decide to rent a space, hire staff, and open a restaurant. What he misses is that restaurant chefs are generally above-average cooks. The home cook may be talented compared to the general population, but in comparison to restaurant chefs, he may be just average (or worse). In a series of experiments, C. Camerer and Lovallo (1999) find that such neglect of the competition causes individuals to enter competitive situations, such as starting a business, much more often than their skills and resources warrant, resulting in large losses (also see, Cain et al., 2015).7 In our terminology, such

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neglect and the poor performance it causes are indicative of low strategic intelligence. In contrast, high strategic intelligence enables one to anticipate the competition. Hence, Hypothesis 2: Higher strategic intelligence improves performance. Analytic Skills Unrelated to Strategic Intelligence One might wonder whether strategic intelligence is just a specific case of analytic skills, just as ease with multiplication and division is just a part of broader mathematical ability. We suggest that is not the case: Research in at least three fields, relying on distinct methods, leads us to expect that strategic intelligence is a distinguished skill, unrelated to analytic skills. First, evidence in neuroscience, from studies of brain imaging, show that analytic skills and strategic intelligence are associated with activity in separate regions of the brain (Bhatt & Camerer, 2005; Bruguier et al., 2010; Coricelli & Nagel, 2009). Second, when researchers used job interviews with prospective CEOs to identify their skills and correlated these skills with subsequent corporate performance, they found that analytic skills (“general ability”), measured by SAT scores and college selectivity, is separate from other cognitive skills related to performance (S. N. Kaplan et al., 2012). Third, that analytic skills and strategic intelligence are separate skills is also evident from the study of individuals on the autism spectrum: Autism manifests itself as a deficit in social cognition (Baron-Cohen, Leslie, & Frith, 1985), and thus necessarily in strategic intelligence. Yet such individuals, who may struggle to impute beliefs to others and predict their behavior, can have normal or even superior analytic skills (e.g., Baron-Cohen, Richler, Bisarya, Gurunathan, & Wheelwright, 2003; Wakabayashi et al., 2007). Hence, Hypothesis 3: Analytic skills and strategic intelligence are uncorrelated.

7

Studying the profitability of films, Brown, Camerer, and Lovallo (2012) found that some filmgoers lack in (what we call) strategic intelligence. Film studios that release films to cinemas but refused to screen them to critics can reap up to a 30% increase in box-office revenues, at least initially. The increase probably owes to some moviegoers who miss the signal: A film that is denied to critics is probably not one of high quality.


When an individual enjoys both high analytic skills and high strategic intelligence, she might benefit from complementarity. For instance, high analytic skills may allow a manager to use financial resources efficiently and coordinate activities across Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

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the firm. High strategic intelligence would allow the same manager to anticipate competitor reaction, and so use these financial resources and firm activities to create a competitive advantage vis-à-vis rivals. In a competitive market, like the one we create here, high analytic skills would allow a market participant to calculate the true value of an asset: The discounted sum of future income generated by the asset is also known as intrinsic, fundamental, or net present value (Fisher, 1907). Yet true value and market price often differ (e.g., Bodie, Kane, & Marcus, 2013), so here strategic intelligence can be lucrative: A market participant who possess high analytical skills with high strategic intelligence would be able to calculate true value, but an insight into the competitors’ mindset would allow her to anticipate how it may differ from market price. Rather, such a participant would use true value as a starting point to predict the highest price that another would pay when selling or the lowest price that a counterparty would accept when buying. Thus, we expect that those with superior analytic skills are better able to benefit from strategic intelligence: Hypothesis 4: Analytic skills and strategic intelligence determine performance complementarily.

Method How Experiments Complement Prior Research Even if few doubt that decision makers differ in their cognitive skills, and even if we agree that those differences affect performance, pinpointing differences and assessing consequences is tricky. Without access to individuals, one cannot obtain direct measurements. So, researchers have sought proxies, attempting to infer skills through biographical information, corporate documents, and surveys. Some researchers, particularly in finance, assessed cognitive skills by reading biographies. Researchers were able to establish a statistical link between firm performance and individual characteristics such as age, education, and experience (Chevalier & Ellison, 1999; A. Goldfarb & Xiao, 2011; cf. Hambrick & Mason, 1984). They found, for instance, that older managers have more conservative policies in investment, financial, and organizational practices, whereas those with an MBA degree are more aggressive (Bertrand & Schoar, 2003). Copyright © 2017 John Wiley & Sons, Ltd.

Other researchers have assessed cognitive capabilities from corporate documents, such as corporate letters to shareholders, annual reports, product announcements, and press releases. For instance, as a proxy for attention, an important cognitive capability (Ocasio, 1997, 2010), researchers frequently analyzed the appearance of certain words in corporate documents (e.g., Adner & Helfat, 2003; Eggers & Kaplan, 2009; S. Kaplan, 2008; S. N. Kaplan et al., 2003). To study the cognitive frameworks of executives, the way they think about industry and competition, researchers have analyzed the contents of news reports and letters to shareholders (e.g., Marcel et al., 2011). To understand how managerial choices shape the performance of new ventures, researchers turned to firm and industry financial data, information about new products, letters, and annual reports to shareholders (e.g., Greve, 1998; Kiss & Barr, 2015, 2017). Others relied on questionnaires. For instance, to study how cultural values affect decision makers’ cognition, researchers surveyed working executives and students in MBA and executive education programs (e.g., Barr & Glynn, 2004; Geletkanycz, 1997; Houghton, Stuart, & Barr, 2010; Schneider & de Meyer, 1991). To assess the effect of personality traits, one study mined a data set of job interviews with prospective CEOs to assess traits, and tied those to subsequent firm performance (S. N. Kaplan et al., 2012). These methods have brought us valuable data. Managerial documents can teach us about the way decision makers perceive themselves, their firms, and their environment—and how this perception compares with objective measures. Similarly, surveys provide self-reported measures that can be similarly compared to objective ones. To these methods, we add experimental evidence. Experiments offer advantages that have made them quintessential in psychology, common in economics, and recommended as a tool for strategy researchers (Croson, Anand, & Agarwal, 2007). Experiments excel in reducing confounds, which are common in naturally occurring data, through a controlled environment and randomized assignment of participants. Because of these advantages, experiments can pinpoint causality, the gold standard of science (List, 2008). Experiments allow tight control of decision environments, but also enable researchers to collect detailed data about participants and test them in interactive decision tasks, as we do here. Indeed, experiments excel in “testing (game theoretic) models and behavioral Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

Strategic Intelligence assumptions,” noted Falk and Heckman (2009, p. 537), the latter renowned for his statistical solutions to nonrandomization (selection bias) in naturally occurring data. Experiments are often easy (and cheap) to replicate, so they can sieve true findings from false positives, an urgent need in the social sciences (Bettis, Helfat, & Shaver, 2016; Lewin et al., 2016). Experiments also reduce the risk of common method bias: variance that stems from the measurement method rather than the constructs that the measures represent (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). One assessment of common method variance estimated that approximately one quarter (26.3%) of the variance in a typical research measure may be the result of systematic measurement errors (Cote & Buckley, 1987). For assessing the effect of cognition on performance, experiments complement content analysis, questionnaires, and archival data. This, perhaps, was the reason Cyert et al. (1959, p. 94) opined that “there is room for substantial basic research in the laboratory on human decision-making under the conditions found in business organizations.” In a review article, Gavetti, Greve, Levinthal, and Ocasio (2012, p. 21) bemoaned how “experiments are perhaps underutilized as a source of new evidence,” especially with the useful results obtained experimentally elsewhere. Considerations for Measurements, Incentives, and Non-deception If measuring the effect of individuals on firm performance is hard, it may be even harder to link managerial cognitive skills to performance (Helfat & Martin, 2015). So, we chose an experimental design suitable for measuring analytic skills and strategic intelligence separately from performance in competition. These measures, both dependent and independent, were mostly observed through actual behavior, such as making choices with real monetary outcomes (induced value; V. L. Smith, 1976), complementing measures that rely on self-reporting, whether in responding to a survey or issuing a letter to shareholders (sometimes written by a team, see Kaplan, Murray, & Henderson, 2003, p. 215). We insisted on clear separation between measurements of cognitive skills and measurements of performance. This separation is not easy to achieve using survey or archival data. In surveys, Copyright © 2017 John Wiley & Sons, Ltd.

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results can be undermined by recall bias (Golden, 1992) and nonresponse, for instance, by managers in failing companies. In archival data, validity is challenged by selection bias (managers are not randomly assigned to firms: capable managers and high-performing firms are likely to choose each other) and survival bias (exceptionally incapable managers seek an alternative career; poorly managed firms go bankrupt). Helfat and Martin (2015) warned of a potential tautology when dynamic managerial capabilities are assumed to be reflected in firm performance. Yet the experimental setting allowed us to simultaneously examine individual skills and actions—and their aggregate effect on the market. In both studies, we combined self-reporting with an induced-value approach (V. L. Smith, 1976). Per this approach, common in economic experiments, participant decisions must carry economic value. When their cognitive skills were assessed and when trading, participants made choices that affected the ultimate cash payment each received. That is, the choices participants made had real and known effects on their compensation.8 Camerer and colleagues (2016), who, in a landmark study, assessed the replicablity of economic experimental, attribute the evident robustness to financial incentives (p. 1436). As recommended (Ortmann & Hertwig, 2002), neither study involved deception, so the risk of subject contamination was low, yet we still asked participants not to discuss the study with others. The experiments were reviewed and approved by the Institutional Review Board. Considerations in Market Design The experimental setting provides ample control, and we used it to suppress confounds and alternative explanations. One important instrument was the design of the experimental market. To control for sources of advantage that are unrelated to cognition, such as market structure or firm 8 Incentives, such as cash or course credit, are common in experimental economics and in psychology. Cash has the added benefit of being unambiguous, infinitely divisible, and useful as a motivating agent (Edwards, 1961). In economic experiments, cash payments are ubiquitous, considered necessary for participants to behave as they normally would (Ariely & Norton, 2007; Hertwig & Ortmann, 2001). Ethical considerations prohibit experiments in which participants may lose money, yet research has found that this constraint has little effect on the kind of findings discussed here (e.g., Ackert, Charupat, Church, & Deaves, 2006; Corgnet, Gonzalez, Kujal, & Porter, 2015).

Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

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resources, we created a market featuring the five definitive characteristics of a competitive market: Competitors are atomistic, products are homogenous, information is complete and public, everybody has equal access to technology and resources, and entry is free (Cabral, 2000, pp. 85–86). Through the market design we also eliminated ambiguity (or uncertainty), because we provided the distribution of future cash flows from assets, so market participants faced risk, which can be handled through such techniques as net present value, but they did not face uncertainty, to which there is no analytical solution (Ellsberg, 1961; Knight, 1921). Such skeletonic markets, where competitive advantage and ambiguity are stripped away, have been used to answer other questions in economics, including in industrial organization (e.g., Holt, 1995; Williams & Smith, 1984). To the five characteristics of a model competitive market, we added the trading mechanism of a double-auction market, based on the seminal design of Smith and his associates (V. L. Smith, 1962; V. L. Smith, Suchanek, & Williams, 1988), replicating a design recently used elsewhere (Dufwenberg, Lindqvist, & Moore, 2005; Levine & Zajac, 2008; Levine et al., 2014). In such a market, each participant may act simultaneously as a buyer or a seller, much as traders do in financial markets. By combining atomicity, product homogeneity, perfect information, equal access to technologies and resources, and free entry with simultaneous buying and selling, we created a market known to possess characteristics of extreme competitiveness (Holt, 1995). Because similar markets have been used often in economic experiments, we know that trading patterns are robust to myriad conditions, including constant value of assets, short-selling, margin (credit) buying, equal portfolio endowment, trading fees, dividend certainty, limitations imposed to reduce price changes, the presence of informed insiders, lengthy trading periods, or a large number of traders (van Boening, Williams, & LaMaster, 1993; King, Smith, Williams, & van Boening, 1993; Lei, Noussair, & Plott, 2001; Noussair, Robin, & Ruffieux, 2001; D. P. Porter & Smith, 2003). We created such a competitive market for a reason. It tests, very cautiously, the possibility that cognitive skills affect performance. Because a competitive market eliminates opportunities for competitive advantage, it makes performance differences less likely (recall that in a perfectly competitive market, economic profit is completely Copyright © 2017 John Wiley & Sons, Ltd.

homogenous). The market was complete: There were negligible transaction costs, perfect information, and a price for every asset in every possible state of the world (Debreu, 1959). Researchers have commonly assumed that in complete markets, strategic opportunities cannot exist (Denrell, Fang, & Winter, 2003; see review in Gavetti, 2012). Ultimately, the market design allowed us to control for three of the four sources of competitive advantage (Peteraf, 1993): The design eliminates ex-post limits to competition, imperfect resource mobility, and ex-ante limits to competition. But what if superior cognitive skills allow some to derive high (supernormal) profits even in such conditions? Because we could measure skill differences ex-ante, before competition begins, and observe differences in performance when competing, we were better able to pinpoint a link between cognition and performance. Because this is a cautious test, any effect can be plausibly generalized to markets that are less competitive, as are the markets typically studied by strategy scholars. Two-Study Design The combination of instruments and markets is novel, so we chose a two-study design. The first study was simple, but it allowed us to verify instruments and test straightforward hypotheses. The second study afforded an opportunity for elaboration. The two-study design provided another advantage: a test of replicability for the main findings. Scholars are increasingly concerned about the validity of scientific findings (Bettis, 2012; Levine, 2012; Lewin et al., 2016), and replications play an important role in ascertaining validity (C. F. Camerer et al., 2016; B. Goldfarb & King, 2016; Open Science Collaboration, 2015). Research and theory have shown that the direct replication we employ here—using a similar study design across different populations—plays a role that is distinct from that of conceptual replications, which test the same constructs using different designs (Simmons, Nelson, & Simonsohn, 2011; Simons, 2014). Sample Size The first study consisted of 16 markets (experimental sessions) of six traders each, and the second study consisted of 9 markets, equally sized. The first study was simple, allowing a larger sample. We used the second study to elaborate the design, refining Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

Strategic Intelligence instruments and adding measures, in addition to the benefits of direct replication. This design required more time from participants, demanded higher pay, and therefore a smaller sample, yet one that is comparable in size to previous studies. At 25 markets, the sample is at least as large as those in other studies using the same method. For instance, V. L. Smith et al. (1988) used a market design similar to ours. They designed markets of 9 or 12 traders, with an average of about five markets per treatment. Dufwenberg et al. (2005) tested two treatments with five markets of six participants each per treatment, a total of 30 participants per treatment. In a large study, Levine et al. (2014) conducted an average of 15 markets per treatment, with each market composed of six traders, as we do. To reduce the risk of false positives, sample size was determined in advance, before results were known (Simmons et al., 2011). Recruitment and Participants For both studies, we recruited from the entire student and staff body of a university for what was described as a market trading experience. As an extra dose of caution, we recruited in two sites: Study 1 was conducted in a large, private U.S. university, which we will refer to as “Ivy”; Study 2 was conducted in a smaller, regional campus of a public university, which we call “Community.” The different sites provided two methodological benefits. The first relates to exposure to experiments. As is common in research universities, many studies were conducted on the campus of Ivy. Potential participants may have taken part in similar experiments and learned to score well on instruments used to assess analytical skill and strategic intelligence. In contrast, no experimental studies have been run on the campus of Community in the preceding 5 years. The second benefit relates to the composition of the participant pool, which should reflect the populations to which we wish to generalize results. At Ivy, a highly selective institution, the participant pool was naturally composed of individuals at the upper end of cognitive skills. Those who work or study at an elite university are not a random sample of the general population.9 This selection bias is unavoidable, but it 9

The proposition that students and executives emerge from the same population is supported by broad evidence that students perform indistinguishably from executives in a wide variety


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undermines generalizing the results to a population other than graduates of elite universities. Even if graduates of such institutions are frequently among top executives and entrepreneurs, we sought a sample more representative of the general population. We found that at the site of Community, a much less selective institution, where participants resembled the surrounding community from which they came. We took an extra step of comparing the sample at Community to the general population in the region and the entire country. We found effectively no differences on important characteristics, including educational level, employment, and income. Thus, the results of Study 2 are more readily generalizable, addressing a common concern about laboratory experiments (Gordon, Slade, & Schmitt, 1986; Greenberg, 1987; Sears, 1986). Measurements and Instruments In both studies, we used three sets of instruments to separately measure analytic skills and strategic intelligence, and investigate how each predicts performance in a meaningful behavioral task: performance in a highly competitive market. By measuring analytic skills and strategic intelligence independently and ex-ante, we could assess the extent to which they are independent and predictive of performance.10 Study 2 expanded the set of instruments used in Study 1 to increase validity and reliability. For a summary of the hypothesized relationships and empirical measures, see Figure 1. Measurements of analytic skills. To measure analytic skills, we used four instruments: a financial-literacy questionnaire (both studies), a newly developed individual version of The Race to 100 (Study 2), The Cognitive Reflection Test (CRT; Study 2), and the Need for Cognition scale (Study 2). Financial literacy is a measure of domain-specific knowledge. The Race to 100 and the CRT add measures for two skills that are broader in application, useful across domains, and of managerial tasks (for reviews and evidence, see Bolton, Ockenfels, & Thonemann, 2012; Fréchette, 2015, 2016). 10 Past studies have assessed performance in strategic tasks, but did not separate analytic skills from strategic intelligence (e.g., Bosch-Domènech et al., 2002; Nagel, 1995). Some brain imaging studies have measured both, but did not link them to performance (e.g., Bhatt & Camerer, 2005; Bruguier et al., 2010; Coricelli & Nagel, 2009). Plus, the method limits their measurements to a handful of participants. A recent study measured both and focused on contribution to performance in the Beauty Contest (Gill & Prowse, 2016). Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

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Analytic skill Measured by a combination of The Race to 100, Cognitive Reflection Test, financial literacy, and Need for Cognition

Performance Measured by cash earnings in an experimental market

Strategic intelligence Measured by distance from the winning number in Guessing Game (one measure in Study 1; two in Study 2)

Figure 1.

Hypothesized relationships and empirical measures of analytic skills, strategic intelligence, and performance.

important for strategy: The Race to 100 measures the analytic skill necessary for backward induction, a cornerstone of game theory. The CRT measures the ability for contemplative or mindful behavior (Laureiro-Martínez, 2014; Levinthal & Rerup, 2006; Winter, 2013). For the first three instruments, we informed the participants that their choices would affect their ultimate cash payment, as a way of motivating effort and in line with the induced-value approach (V. L. Smith, 1976). The last instrument asks about one’s agreement with statements, so it is impossible to assess accuracy. The financial literacy questionnaire, which has been used elsewhere (Levine et al., 2014), included 10 simple financial scenarios, which asked a participant to name a price, for example, “In the 4th period, someone wants to sell you his stock. Write the maximum price you will be willing to pay for it.” The accuracy of a participant’s responses was a measure of her ability to price assets. For each participant, we calculated a financial literacy score as the negative of squared errors in the questionnaire. Backward induction, “the oldest idea in game theory” (Aumann, 1995, p. 6), is a method for solving a dynamic optimization problem. It instructs to start with the desired future outcome and work backward to figure out how to act in the present to reach that outcome. Backward induction can be used in situations that are nonstrategic, where one Copyright © 2017 John Wiley & Sons, Ltd.

simply optimizes; or strategic, as in the Guessing Game. To succeed in the Guessing Game, a player must (a) use backward induction and (b) correctly anticipate what the others will do. But for many people, using backward induction is not natural. Researchers have documented that people differ in their ability to evoke backward induction, instead relying on “limited lookahead” or just guessing (Ho & Su, 2013; Johnson, Camerer, Sen, & Rymon, 2002; S. D. Levitt, List, & Sadoff, 2011; Nagel & Tang, 1998). The Race to 100 has been used elsewhere to measure the analytic ability to engage in backward induction (Gneezy, Rustichini, & Vostroknutov, 2010; S. D. Levitt et al., 2011; Sally & Sally, 2003). We converted the task to a single person version, which isolates each participant’s analytic skill without requiring strategic intelligence. This is because the task has a dominant strategy that can be found through backward induction: A player’s best response is unrelated to others’ actions or beliefs about them, so strategic intelligence is irrelevant. If one grasps the dominant strategy, success is certain. If one attempts to respond without backward induction, the scenarios place an increasingly impossible cognitive load. To experience that, the reader may try the task: In it, a participant and a hypothetical rival take turns choosing a number between one and nine. The numbers are added, and the winner Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

Strategic Intelligence is the player who reaches 100. To win, a player must backward induce: starting at 100 and working in reverse to understand which number should be chosen to eventually reach that goal. We presented each participant with 10 scenarios of actions by a competitor (e.g., 94, 83, 73 … 13), and asked her about the best response. A participant’s score, ranging 0–10, is a measure of her ability to backward induce (the solution appears in a footnote).11 A direct measure of one’s ability to reason is the Cognitive Reflection Test (CRT; Frederick, 2005), which presents problems that seem to have intuitive—but incorrect—answers. The test measures the extent to which one relies on simple heuristics, stemming from the cognitive process known as System 1, which offers quick decisions with little conscious deliberation (Kahneman, 2003; Stanovich & West, 2000), versus System 2, the more attentive and deliberative faculty. In strategy, this distinction underlies the formations of habits and routines, conscious deliberation and mindfulness, and behavioral strategies. All of these affect individual and organizational performance (Greve, 2013; Laureiro-Martínez, 2014; Levinthal & Rerup, 2006; Winter, 2013). In addition to its theoretical value, CRT possesses an important practical feature: The responses to its few questions correlate strongly with more burdensome measures of analytic skill, such as SAT score (and its mathematical and verbal subcomponents), ACT score, and the Wonderlic Personnel Test. When it comes to aspects of economic decision making, such as intertemporal choice and choice under uncertainty, CRT has been shown to be the best or second-best predictor of behavior, and the only one that is related to all of the decision-making domains tested (Frederick, 2005, p. 36). CRT requires just a few minutes yet offers predictive power that equals or exceeds those of much longer tests. For its theoretical importance and practical flexibility, it has become a common measure of analytic skill (e.g., Brañas-Garza et al., 2012; Laureiro-Martínez, 2014). The Need for Cognition (NFC) scale, an instrument used in social psychology, measures self-reported interest in thinking tasks through endorsements of statements such as “the notion of thinking abstractly is appealing to me” (Cacioppo

11 In The Race to 100, the dominant strategy is to choose a number that brings the total to a multiple of ten. Using this strategy, one is guaranteed to win, no matter what any other player chooses.


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& Petty, 1982; Cacioppo, Petty, & Kao, 1984; Sadowski, 1992). Measurement of strategic intelligence. For measurement, we relied on the Guessing Game, which, as discussed above, is a well-established measure, substantiated in brain imaging and widely replicated. In Study 1, we asked each participant to pick a number between 0 and 100 so that her number is closest to two-thirds the average of numbers submitted by all other participants (not just those who were later assigned to trade with her). In Study 2, we increased reliability by repeating this test and adding a second one: asking the participant to choose a number between 0 and 360 so that her number is closest to three quarters the average of all numbers submitted. With either measure, the best answer depends on the choices of the other players, unlike in nonstrategic games such as The Race to 100. Again, in line with induced-value theory (V. L. Smith, 1976), we motivated with an incentive: The participant whose response was closest to the winning number won a prize. To further increase reliability, we varied the prize in $5 increments between $5 and $20 (prize size has no effect on performance; Kruskal-Wallis p > .5). During the experimental sessions, we did not inform participants of their level of strategic intelligence. After we obtained the responses, we created a measure of strategic intelligence: For each response of each participant in each test, we calculated the negative of the squared difference between the response and the winning number (two-thirds or three-quarters the average of all numbers submitted). In Study 2, we obtained a compound measure by averaging the two scores, which were strongly correlated, as expected (𝜌 = 0.432, p < .001). The measure, of course, is a simplification, but it captures two important features of strategic decision-making. First, the optimal course of action depends on the actions of competitors (Cyert et al., 1959, p. 85). Second, these actions are ambiguous (Levinthal, 2011). An experiment seeks to simplify a complex world so that the mechanisms governing it can be elucidated. But our simplification introduces a conservative bias: If participants have troubles solving simple problems, it is hard to see how they could excel in solving complex ones. So, simple as this approach may appear, much of the advances in decision making and behavioral economics have relied on examining relatively simple decisions, as Levinthal (2011) notes. Prospect theory (Kahneman & Tversky, 1979), supported by Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

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self-reported reports about choices of simple gambles, is a prime example. Measurement of performance. In both studies, we measured performance in a straightforward manner: profit from trading. This sidelines the common conundrums about the right dependent variable (Powell, 2003). The experimental design reduces the risk of random variation perceived as meaningful. As discussed above, we purposefully constructed a market designed for high competitiveness. By doing so, we isolate the effects of cognition: because many alternative explanations are controlled for (Table 1), differences in performance must be related to individual differences. And because the market is likely more competitive than most real-world markets, the results likely underestimate the effect of individual difference on performance. We expect that cognition would matter more in markets that are less competitive, and hence more susceptible to cognitive maneuvering. The Experimental Procedure Cognitive measures. In both studies, we followed an identical procedure. We recruited participants from a pool that included the entire student and staff body of a university (as explained above, each study was conducted at a different site). Through email messages and posters advertising a market trading experiment, members of the participant pool received an Internet link. The link directed them to an online questionnaire that measured analytic skills and collected general demographic information. To minimize order effects, we randomized the order in which the instruments were presented to each participant (a technique known as counterbalancing, see Krosnick & Presser, 2010). To reduce the risk of demand characteristics and other biases (Orne, 1962), the online questionnaires were completed prior to trading. Trading and performance. We waited at least 3 days between the completion of the cognitive and strategic intelligence instruments and the trading sessions, as a control for the known effects of mood (e.g., Hirshleifer & Shumway, 2003) and demand characteristics (Orne, 1962). Meanwhile, we randomly assigned participants to trading sessions and informed them of the time and the place. Randomization is critical for the experimental method, because the random assignment, on average, Copyright © 2017 John Wiley & Sons, Ltd.

eliminates endogeneity that may occur in natural settings (Kirk, 2003; Shadish, Cook, & Campbell, 2002). When examining naturally occurring data, separating the effects is hard (Helfat & Martin, 2015). Random assignment makes such confounds random, so that any spurious effect should average zero. Control variables, common when analyzing archival data, are less important here. As in prior experimental designs (e.g., Dufwenberg et al., 2005; Levine & Zajac, 2008; Levine et al., 2014), each market consisted of six participants, each of whom was endowed with cash and assets before trading began. The assets can be thought of as almost anything that has a probabilistic return and a limited lifespan. For instance, a patent or product innovation can generate a revenue stream, for which a probability distribution can be calculated, for a known period. As in real markets, one could earn profits (dividends) from holding the assets, but one could also earn by trading them. Each participant was incentivized to maximize her profits from dividends and trading, all of which were paid in cash at the end of each session. Upon arrival to the trading laboratory, participants were instructed not to speak with each other and we did not introduce them to each other. As soon as six participants were present, we sat them in cubicles, separated by above-eye-level walls, in front of networked computers. We provided the participants with detailed instructions that included the information necessary to calculate the true value of all the assets traded: We designed the assets such that each paid a dividend of 20 points or nothing, with equal probability at the end of each trading period. Because the distribution of future cash flows and lifespan were known, participants could use calculate an asset’s true value with precision, using expected value and net present value, a standard approach in finance. Their ability to do that—to price assets accurately—was measured by the financial literacy questionnaire. The instructions went further: providing the average cash stream in each period, which represents the value of the asset, stressing that assets had no residual value, and reminding that each participant’s profits will be paid in cash at the end of the session.12 After participants 12 A brief reminder of expected value and net present value calculations: Because the expected dividend per period is 20 × 0.5 = 10 points, each asset is expected to generate a stream of dividends equal on average to 10 × 10 + 0 = 100 points over its life of 10 periods with no residual value. Thus, as the asset nears its end of


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Table 1 How Experimental Design Reflects the Theoretical Characteristics of a Highly Competitive Market Characteristics of a highly competitive market (Cabral, 2000; Farnham, 2014; Mankiw, 2015)

Present in the experimental design

Atomicity

Yes

Product homogeneity

Yes

Perfect information

Yes

Equal access to resources

Yes

Free entry

Yes

Experimental design Market participants cannot unilaterally set prices, neither individually nor through collusion. Each participant can only make offers to buy or sell by placing bids and asks, which must be accepted by another. Collusion is thwarted by limiting interaction: Participants, who are randomly assigned to a market, sit in cubicles separated by above-eye-level walls. They may not communicate with each other. In trading, their counterparts are anonymous The traded assets are identical and cannot be altered. Participants receive, without deception, all the information needed to calculate values All price information is publicly available to all market participants, eliminating the possibility of arbitrage: Bids and asks appear as soon as they are entered; when deals are struck, prices are immediately visible by everybody Market participants have equal access to assets. The performance of the assets is exogenous and known publicly and in advance. Developing or acquiring new assets is not possible All traders have equal endowments and free access to the market. There are no barriers to entry or exit

read the instructions, we introduced the financial literacy questionnaire, which all of them completed within less than 10 min. Next, participants underwent training in the mechanics of trading and were given a chance to ask questions. Then, trading began. The market was programmed and conducted in z-Tree (Fischbacher, life, its present value decreases. Whether the assets are designed with value, the decreases over time or constant has little influence on trading patterns, prior research has shown (Noussair et al., 2001). Copyright © 2017 John Wiley & Sons, Ltd.

If this characteristic were weaker, competition would be reduced Individuals or firms can set prices by virtue of being a monopoly or through collusion with others (A. Smith, 1904 [1904]: I.10.82). Relationships can be a source of competitive advantage (Burt, 2005; Dyer & Singh, 1998; Uzzi & Gillespie, 2002)

Product differentiation, e.g., through branding or technological innovation, can bring a competitive advantage (D. P. Porter, 1980; Schmalensee, 1982) Individuals or firms can build a competitive advantage through differentiated access to information and knowledge (Alchian & Demsetz, 1972; Argote & Ingram, 2000; Grossman & Stiglitz, 1980; Winter, 1987) Individuals or firms can enjoy a competitive advantage from securing exclusive access to resources and capabilities (Barney, 1996; Grant, 1996; Peteraf, 1993; Winter, 1995) Market structure can provide an advantage by limiting competition, e.g., by raising barriers to entry (Demsetz, 1982; D. P. Porter, 1980)

2007), a computer-based platform for experiments (see Figure 2 for a sample trading screen). In this double-auction market, as common in highly competitive financial markets, each participant could simultaneously act as a buyer and a seller. Using the computer in front of her, a participant could place one or more offers to sell (“an ask”, in trading parlance). Simultaneously, she could also place one or more offers to buy (“a bid”). At the same time, she could initiate a transaction by accepting an ask or a bid placed by another participant. Buyers and sellers were anonymized, to thwart collusion, but all price Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

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Figure 2. Sample trading screen, showing (1) the amount of Cash (top left corner) and (2) the number of assets that a participant holds (Shares). Towards the middle of the screen, a participant can see all current (3) asks (Offers to Sell) and (4) bids (Requests to Buy), as well as (5) all completed transactions (Trading Price). Each market participant can make offers to sell or to buy by clicking on the leftmost or rightmost buttons. A participant can also respond to such offers a clicking Buy or Sell. To facilitate replications and extensions, we shared this instrument (doi: 10.17605/OSF.IO/P49YA).

and trading information was publicly available to all participants, eliminating the possibility of arbitrage: Bids and asks appeared as soon as they were entered; when deals were struck, prices popped up immediately for everybody to see. Participants worked at their own pace—initiating, observing, responding, and transacting whenever they wished. Bids and asks were entered, responded to, and executed continuously throughout the trading periods. Trading consisted of one practice period and 10 real periods of 120 s each (period length has no discernable effect on trading patterns; van Boening et al., 1993). When the allotted time ended, we calculated each participant’s profits and paid them in cash, together with a show-up payment. As noted, the sample size was determined in advance, before data were collected and results reviewed. As recommended by the Open Science Framework, we report all data exclusions, all manipulations, and all measures in the study. Open Instruments and Open Data To facilitate replications and extensions, we shared instruments, data, and statistical code by permanently depositing them with the Open Science Framework, where they can be freely Copyright © 2017 John Wiley & Sons, Ltd.

accessed (http://doi.org/10.17605/OSF.IO/P49YA). Teaching materials are also available there.

Analysis and Results In such a competitive market theory predicts homogeneity in profits, but we observed that behaviors and outcomes varied greatly. Here are two illustrations: One trader (ID #90101) bought an asset in period 3 of trading. She paid 80 points, a price that was equal to the true value of the asset. Yet, in the very next period, she posted an asking price of 130, a price exceeding true value by 60 points. She sold it instantaneously, reaping an 85% return within one period. Then, she ventured further: In period 7, she bought another asset, this time at a slightly inflated price of 50 points, 10 points above the true value. Then, she offered to sell it immediately, in the same period, at a much higher price. A buyer agreed to pay for it 150 points, giving the seller a gain of 200%. This is what we would expect from a person of high strategic intelligence: She accurately grasped that some traders were less skilled, so she could sell an asset above true value. Recall that arbitrage was impossible, so Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

Strategic Intelligence we must assume that she was willing to overpay when buying because she anticipated selling at a higher price. Contrast that behavior with that of a participant with strong analytic skills—but lower strategic intelligence—who could have calculated that an asset was too expensive, and simply avoided buying it, reasoning that overpriced assets are a poor investment. Another example of strategic intelligence from another session: A trader (ID #90802) posted a low bid and bought an asset in period 3 below true value, at a 15 points (about 20%) discount. Such a decision does not require much strategic intelligence, but merely analytic skills. Yet, he managed to sell the same asset in the next period at 55 points, reaping a quick return of 92%. And who was the buyer? The same trader who sold him the asset in the first place. To recommend “buy low, sell high,” is easy, but grasping that the same person may sell you low and buy high from you necessitates keen insight into others’ behavior—strategic intelligence (and the example demonstrates that strategic intelligence requires speedy action, before the opportunity is snagged by another). Results: Study 1 As detailed above, the first study consisted of 16 markets, of six traders each recruited from the entire student and staff body of Ivy, a large and selective U.S. university (we complemented this sample in Study 2). Analytic skill was measured through the financial literacy instrument, and strategic intelligence was measured through the Guessing Game. Performance was measured as profit, in cash earned. We predicted performance from the ex-ante measures of analytic skills and strategic intelligence. Since market participants can affect each other’s profit, any analysis must account for correlations. To do that, we rely on nonparametric statistical methods. We calculate robust standard errors, also known as heteroscedasticity-consistent standard errors, which provide accurate standard errors (and p values) when modelling errors may be correlated or not uniform (Huber, 1967). We follow the method recommended by White (1984, pp. 134–142) and cluster standard errors on individual, which controls for participant effects. Following recent recommendations (Bettis, Ethiraj, Gambardella, Helfat, & Mitchell, 2016; Cumming, 2014; Lewin et al., 2016), we paid particular attention to effect size. For that, we calculated Copyright © 2017 John Wiley & Sons, Ltd.

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standardized coefficients, which allow comparison of effect size across variables, even if measured on different scales. We assessed the robustness of the results against alternative statistical techniques and found them stable, as detailed in the relevant tables. The results back some of our hypotheses: Competitors with better analytic skill perform better, as predicted by Hypothesis 1. Independently of analytic skill, those with higher strategic intelligence also perform better, as suggested by Hypothesis 2. Analytic skill and strategic intelligence are uncorrelated (𝜌 = 0.016, p = .919), backing Hypothesis 3. By definition, competitors who possess higher strategic intelligence can better assess the intentions of competitors. And with information about the likely behavior of others, those with higher analytic skills can devise better strategies. For that, we hypothesized an interaction effect between the two (Hypothesis 4). Empirically, we find that the interaction between analytic skill and strategic intelligence is positive. Comparing effect size (Table 2, Model 3) shows that analytic skill, strategic intelligence, and the interaction term of the two were important contributors to performance, despite the obvious effects of other individual differences, market-specific circumstances, and luck. Results: Study 2 We aimed to reproduce the results of Study 1 in a different sample, but also to extend them. To test for reliability, we chose the recommended method of direct replication (Simmons et al., 2011; Simons, 2014). To extend the original findings, we elaborated the design, refined some instruments, and added measures. To the financial literacy measure we added three additional measures of analytic skills: The Race to 100, the CRT, and the Need for Cognition scale. We also doubled the measures of strategic intelligence by adding a second measure of performance in the Guessing Game. All these methodological enhancements are detailed in the Method section. Summary statistics and correlations. This study features several new measures, so we assessed correlations between all measures of analytic skills and strategic intelligence (Table 3). The resulting p values were Bonferroni corrected, to counteract the statistical problem inherent in multiple comparisons (Dunn, 1959, 1961). We Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

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Table 2 How Analytic Skill, Strategic Intelligence, and their Interaction Affects Profits in Trading

DV: profits Analytic skill Robust standard errors Exact p values Strategic intelligence

(1) OLS robust standard errors

(2) OLS robust standard errors

(3) OLS robust standard errors standardized coefficients

7.463 (6.609) [.262] 0.0095 (0.00462) [.043]

14.09 (6.719) [.039] 0.044 (0.021) [.038] 0.004 (0.002) [.063] 925.3 (46.24) [.000]

0.204 (6.719) [.039] 0.703 (0.021) [.038] 0.596 (0.002) [.063] 925.3 (46.24) [.000]

Analytic skill × strategic intelligence Constant

871.9 (46.49) [.000]

Note. (1) Ordinary least squares (OLS) regression with robust standard errors, clustered on individual. (2) OLS regression with robust standard errors, clustered on individual, with interaction term. (3) OLS regression with robust standard errors, clustered on individual, with interaction term and standardized coefficients. Robust standard errors, also known as heteroscedasticity-consistent standard errors, provide accurate standard errors (and p values) when modelling errors may be correlated or not uniform. Robust standard errors are in parentheses and exact p values are in square parentheses. Study 1; N = 96. Effects are robust to multiple statistical specifications, including OLS jackknife regression, OLS bootstrap regression, median robust regression, and median bootstrap regression. To facilitate replications and extensions, we deposited the data with the Open Science Framework (http://doi.org/10.17605/OSF.IO/P49YA).

Table 3 Between-instrument Correlations (Spearman’s rho) Cognitive reflection test The race to 100 Financial literacy Need for cognition Strategic intelligence Profits

0.496 0.489 0.098 0.161 0.398

The race to 100 0.390 −0.107 0.046 0.514

Financial literacy

Need for cognition

−0.030 −0.107 0.595

−0.064 0.044

Strategic intelligence

0.162

Note. Study 2; N = 53 (one market had five participants). To facilitate replications and extensions, we deposited the data with the Open Science Framework (http://doi.org/10.17605/OSF.IO/P49YA).

find that two of the new measures—CRT and The Race to 100—correlate highly with the measure of financial literacy and with each other (Table 3). This is an indication of internal validity, supporting our assertion that they all measure the same underlying construct—analytic skill. In contrast, Need for Cognition does not correlate with any of the other measures. This is not entirely surprising, because the former is a self-reported measure, one’s self-assessment of engaging in cognitive tasks, whereas the others are behavioral measures, which directly measure performance. We hypothesized that analytic skill is unrelated to strategic intelligence (Hypothesis 3). We find so in Study 1, and here again we find that none of Copyright © 2017 John Wiley & Sons, Ltd.

the measures of analytic skill is much related to strategic intelligence, providing additional support. Those with high analytic skills do not necessarily enjoy higher strategic intelligence. Regression. As we did earlier, we used analytic skills and strategic intelligence to predict performance, using ordinary least squares (OLS) regressions with robust standard errors clustered on individual. To allow comparison between effect sizes, we calculated standardized coefficients. We began by comparing the influence of strategic intelligence and three measures of analytic skill: CRT score, The Race to 100 score, and financial literacy score, the exclusive measure used in Study 1. As hypothesized and found in Study 1, strategic Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

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Table 4 Ordinary Least Square Regressions of Total Profits, Profits from Trading, and Earnings from Dividends (Standardized Coefficients)

DV: profits Analytic skill Robust standard error p value Strategic intelligence CRT score The race to 100 score Financial literacy score

(1)

0.264 (0.388) [.002] 0.080 (0.660) [.559] 0.318 (0.292) [.034] 0.326 (0.423) [.013]

(2)

(3)

(4)

(5) DV: only profits from trading

0.568 (0.436) [.000] 0.256 (0.393) [.003]

0.554 (0.438) [.000] 0.237 (0.398) [.006]

0.567 (0.450) [.000] 0.242 (0.381) [.004]

0.554 (0.384) [.000] 0.190 (0.340) [.013]

0.136 (0.286) [.319] 0.080 (0.243) [.559]

0.098 (0.336) [.274]

0.140 (0.243) [.042]

−0.083 (0.300) [.646]

12.140 (0.627) [.000]

9.182 (0.618) [.000]

2.955 (0.342) [.000]

Analytic skill × strategic intelligence Need for cognition Constant

19.563 (4.609) [.000]

0.145 (0.687) [.241] 6.479 (4.640) [.169]

12.140 (0.625) [.000]

(6) DV: only earnings from dividends

Note. Ordinary least squares (OLS) regression with robust standard errors, clustered on individual (Study 2). To aid interpretation of effect size, results shown using standardized coefficients. Robust standard errors are in parentheses and exact p values are in square parentheses. N = 52 (two participants omitted due to missing data). To facilitate replications and extensions, we deposited the data with the Open Science Framework (http://doi.org/10.17605/OSF.IO/P49YA).

intelligence predicts performance. So do two of the measures of analytic skill (Table 4, Model 1). The three measures are highly correlated, risking multicollinearity (and perhaps explaining the low statistical significance of the CRT score). So we used principal component analysis to aggregate them into a single measure of analytic skill. Strategic intelligence and the combined measure of analytic skill are good predictors of performance (Table 4, Model 2). The only construct with no predictive power is Need for Cognition, which is excluded from further analysis (Table 4, Model 3). To better understand the effect of strategic intelligence, we proceeded to introduce explicit controls for one element of luck in performance: earnings from dividends. As described earlier, in any given round an asset may or may not pay a dividend with equal probability. That means, for instance, that a participant may reap a windfall by chance, Copyright © 2017 John Wiley & Sons, Ltd.

just because she held many assets when a dividend happened to be paid. Such an outcome results from luck, not analytic skill or strategic intelligence. To separate the two, we calculated for each participant earnings from dividends and profits from trading. This distinction becomes important when we examine the interaction between strategic intelligence and analytic skill (Hypothesis 4). In the data set that combines earnings from dividends and trading (Table 4, Model 4), the interaction term has low statistical significance (p < .27). But when we separate earnings from dividends and profits from trading, the picture becomes clearer. Unsurprisingly, the interaction of strategic intelligence and analytic skill does not affect earnings from dividends (Table 4, Model 6), a matter of luck. But strategic intelligence matters greatly for profit from trading (Table 4, Model 5), a matter of skill. We interpret the interaction to mean that when one can Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

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Table 5 The Effects of Analytic Skills and Strategic Intelligence on Surplus in Trading (Standardized Coefficients)

DV Analytic skill Robust standard error p value Strategic intelligence Analytic skill × strategic intelligence Constant n

(1) Surplus

(1a) Surplus in first half of trading

(1b) Surplus in second half of trading

(2) Surplus as seller

(3) Surplus as buyer

(4) Surplus as contract maker

(5) Surplus as contract taker

0.074 (0.023) [.000] 0.037 (0.026) [.027] 0.022 (0.0171) [.106] 0.0638 (0.0479) [.188] 2,050

0.238 (0.0198) [.000] 0.098 (0.0141) [.0101] 0.128 (0.0104) [.000] 0.0469 (0.0290) [.112] 1,031

0.064 (0.0403) [.000163] 0.033 (0.0541) [.139] 0.005 (0.0381) [.782] 0.0891 (0.0802) [.272] 1,019

−0.018 (0.075) [.725] 0.115 (0.053) [.003] 0.044 (0.037) [.188] 0.996 (0.126) [.000] 1,025

0.148 (0.086) [.008] −0.023 (0.059) [.572] −0.005 (0.049) [.907] −0.868 (0.098) [.000] 1,025

0.061 (0.036) [.010] 0.063 (0.045) [.008] 0.002 (0.029) [.896] 0.204 (0.074) [.008] 1,008

0.086 (0.045) [.000] 0.003 (0.042) [.919] 0.042 (0.030) [.117] −0.091 (0.074) [.229] 1,042

Note. Ordinary least squares (OLS) regression with robust standard errors, clustered on individual (Study 2). To aid interpretation of effect size, results shown using standardized coefficients. Robust standard errors are in parentheses and exact p values are in square parentheses. To facilitate replications and extensions, we deposited the data with the Open Science Framework (http://doi.org/10.17605/OSF.IO/P49YA).

calculate true value and understand what the market will bear, one has greater advantage. Those who are endowed with high analytic skills and strategic intelligence can determine true value for an asset, and then anticipate how low a price a seller would accept or how high a price a buyer will pay. Having one skill but not the other is less than ideal. Effect size. Comparison of standardized effect size shows that strategic intelligence is a strong predictor of performance, its magnitude like that of financial literacy or the critical cognitive skill of backward induction (Table 4, Model 1). When the three independent measures are combined into a single score of analytic skill, that measure becomes the strongest predictor of performance. Yet strategic intelligence still looms large, accounting for about half of the combined effect of analytic skill (Table 4, Model 3). How Strategic Intelligence Affects Performance: Further (Post-Hoc) Analysis We established that high analytic skills and strategic intelligence boost profits even in a market that epitomizes economic efficiency and thus—competitiveness. But how, exactly? We dug deeper, switching from overall performance in trading to performance in each transaction. To do that, we linked individual-level data, collected ex-ante, Copyright © 2017 John Wiley & Sons, Ltd.

with transaction-level data, collected in real time during trading. For each transaction, we captured the market price, the corresponding true value, the identity of buyer, and the identity of seller. We then calculated the surplus in each trade and examined how it was split between the buyer and the seller. The surplus is the discrepancy, if any, between the market price—the price at which the seller agreed to sell and the buyer agreed to buy—and the true value. If the surplus is different from zero, it means that the asset was not transacted at its true value: The buyer paid too much or the seller received too little. And, of course, the counterpart received too much or paid too little because the buyer and the seller surpluses are mirror images: If the seller surplus was positive, then the buyer surplus was negative, and vice versa. To find whether the surplus was captured by the seller, we subtracted true value from the selling price. If the seller sold above true value, then the surplus is positive; if the sale was at true value, then the surplus is zero; and if the sale was below true value, then the surplus is negative. For the buyer, we reversed the terms to keep the measure consistent. The buyer’s surplus was calculated by subtracting the transaction price from true value. A positive surplus meant that the buyer profited from buying an asset below its true value; a negative surplus meant Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

Strategic Intelligence that the buyer overpaid. Ultimately, we wanted to understand how some traders could repeatedly buy below true value (positive buyer surplus), sell above true value (positive seller surplus), or both, accumulating cash and outperforming competitors. As the table shows, those with higher analytic skill or higher strategic intelligence (or both) are more likely to profit, and the interaction between analytic skill and strategic intelligence is prominent early (Table 5, Models 1, 1a). But when we examine the source of surplus for buyers versus sellers, we find a clear distinction (Table 5, Model 2): For sellers, strategic intelligence has a sizable effect—on average, an increase of one standard deviation boosts surplus by 11.5% (p < .003). In contrast, analytic skill had only a weak effect. For buyers, the picture is reversed (Table 5, Model 3): Analytic skill has a large effect, strategic intelligence does not. Strategic intelligence plays a different role for buyers and sellers, we reason, because it matters who initiates a transaction and who accepts it. It seems that the trader initiating an offer, whether an offer to buy (bid) or sell (ask), can benefit more from strategic intelligence. To test that, for each transaction we coded which side initiated the transaction (by posting a bid or an ask) and which side responded to an offer. Next, we examined the effect of analytic skill and strategic intelligence on those who made an offer (Table 5, Model 4) versus those who took an offer (Table 5, Model 5). We find that for makers, both analytic skill and strategic intelligence contribute to surplus, in almost equal parts. For takers, analytic skill matters, whereas strategic intelligence matters only as it interacts with analytic skill. This makes sense: Whether selling or buying, the offer maker is the one who determines the price. In this experimental design, as in many markets, the taker can only choose to accept the offer or ignore it. So, the maker can rely on analytic skill to determine the true value of the asset, and on strategic intelligence to determine what price a counterpart will accept. The better the maker is in ascertaining true value—and the better she is in anticipating the reaction of her counterpart—the larger is her surplus. Conversely, makers who lack in analytic skills may misprice, as in offering to sell too low or requesting to buy at too high a price. Makers who lack strategic intelligence may not realize that others would accept prices above true value (when offering to sell) or below it (when offering to buy). Thus, they Copyright © 2017 John Wiley & Sons, Ltd.

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receive a worse price than they could have gotten, leaving money on the table. For takers, strategic intelligence matters less, probably because a taker cannot change the offer, just accept or reject it, so her surplus can come only from identifying offers that are at odds with true value, and exploiting them. (Had we allowed negotiations, strategic intelligence may have mattered even more as a taker could benefit from anticipating reaction to a counteroffer). In sum, we find that high strategic intelligence increases performance by conferring a substantial advantage for sellers and makers. For buyers and takers, it offers a benefit in conjunction with analytic skills. This fits with theoretical postulations that some people have more accurate beliefs about the environment than others (March & Olsen, 1976), that some firms are better in recognizing sources of potential value in markets (Barney, 1986), and that sensing threats and seizing opportunities are crucial for performance (Teece, 2007, p. 1319). (And see Nobel laureates Akerlof and Shiller (2015) on how some businesses anticipate the bounded rationality of consumers and exploit it.)

Conclusion Where does competitive advantage come from? This question, which lies at the heart of strategy, has been answered by looking at market structure and firm resources, and, recently, individuals and their interactions. We began with an illustration, speculating that Under Armour, a startup born without any obvious strategic resources ex-ante, may have been successful thanks to the managerial cognition of its founder. We proceeded on firmer ground, empirically linking mental processes, one of the least understood areas of managerial cognition (Helfat & Peteraf, 2015), to performance. One mental process is analytic skills. The effect is not entirely surprising: A sizable part of our educational efforts is devoted to improving analytic skills. Whether in elementary school or in MBA programs, we aim to train people to find the right solution. But what if the right choice hinges on the actions and reactions of competitors? Then, another skill gains importance: strategic intelligence. One recent research stream in experimental economics has shown that people differ in their strategic performance. Another, mostly in developmental and cognitive psychology, has shown that they differ in their social cognition. Strategic intelligence is Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

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an application of social cognition in competition. We find that even in a highly competitive, textbook efficient market, some market participants do better than others. Their performance is boosted, separately, by high analytic skill and high strategic intelligence. The two skills are found to be independent but they interact: Market participants who possess high analytic skill benefit the most from strategic intelligence. Strategic Intelligence and Entrepreneurial Performance Research in strategic management and entrepreneurship often envisions opportunities as lying in wait to be discovered (Kirzner, 1997). And this may be a true description of the opportunities that arrive by discovering new products or services, markets, raw materials, or methods of organizing (Eckhardt & Shane, 2003; Schumpeter, 1934 [1912]). But strategic intelligence adds a twist: Advantage can come not only from a superior product or lower cost, it can come from anticipating what competitors will do. Here, success comes not only from beating nature by finding a better solution, but also from besting the competition. Recall the example of resort builders contemplating the most competitive location: One could be most successful not thanks to superior service or cheaper prices, but by anticipating where the others are likely to located, and outsmarting them. When advantage stems from strategic intelligence, it is the result of interaction between competitors. Thus, it is hidden from view and revealed through action. It materializes not merely by searching the environment, but from engaging competitors. To rephrase the metaphor of Alvarez and Barney (2007): Advantage from discovery is akin to mountain climbing, but advantage from strategic intelligence is like competitive mountain building. It does not lay waiting to be discovered, yet it is also not entirely created by a single actor. It requires two elements: an inclination—high degree of strategic intelligence—and a situation—the presence of lesser rivals. So a fitting concept may be actualization: opportunities are propensities that exist independently of potential actors, but they require action (here: interaction) to materialize (Ramoglou & Tsang, 2016). The actualization approach offers vocabulary that may be useful in distinguishing those who possess high analytical skills, strategic intelligence, both, or neither. Copyright © 2017 John Wiley & Sons, Ltd.

They vary in their ability to anticipate whether an opportunity exists; they also differ in assessing their chances of exploiting it (see pp. 428–430). Strategic Intelligence and Organizational Performance Strategic intelligence underlies a dynamic managerial capability—one that affects capabilities such as pricing, planning, value creation—and therefore relates to competitive advantage. These activities exemplify the benefit of strategic intelligence: If you can predict competitors’ prices, you may be able to increase sales and market share. If you can anticipate a competitor’s reaction to the launch of a new product or an entry to a new geographical region, you may be able to preempt it. If you understand how competitors intend to create value, you can deny them from doing so. The value of strategic intelligence may be clear when it comes to entrepreneurs and their nascent organizations, such as Under Armour. But how does it matter for organizations? This question cannot be directly answered by our research, but related answers have appeared elsewhere. Students of organizational theory have often assumed that organizations can do (at least) what individuals can do. The assumption is not always explicit, but it is persistent. One finds frequent borrowing from cognitive and social psychology to strategy and organizational theory. For example, people can learn, and so can organizations (at least some; B. Levitt & March, 1988). People can memorize, attend, sense, detect, search, explore, exploit—and so can organizations (e.g., Carley & Lin, 1997; Gavetti & Levinthal, 2000; Levinthal & March, 1981; March, 1991; Ocasio, 1997; Teece, 2007; Walsh & Ungson, 1991). (The opposite, however, is not true: An organization can have a capability that is not contained in any single member, but rather in organizational routines, hierarchy, memory, or elsewhere). In this light, we would welcome research that studies how the skills of individuals, such as strategic intelligence, can become an organizational capability. Strategic intelligence matters when individuals directly affect organizational performance. Here the hypothesis is straightforward: Entrepreneurs and executives who enjoy higher analytic skills or strategic intelligence (or both) will lead to better organizational performance. But some scholars insist that capabilities must be held by an organization, Strat. Mgmt. J., 38: 2390–2423 (2017) DOI: 10.1002/smj

Strategic Intelligence not a specific individual within it. Because individuals are not permanently attached to an organization, an individual skill is not the same as an organizational capability. At the same time, clearly, individuals and their cognition affect the creation and adaption of organizational capabilities (Zollo & Winter, 2002, p. 341). This is the indirect route, where an organization absorbs individual skills. It is compatible with our evidence. It merely requires the plausible assumption that individual skills, such as strategic intelligence, can become a property of a firm or a collective, an assumption supported in research on procedural memory (M. D. Cohen & Bacdayan, 1994), collective intelligence (Woolley et al., 2010), open collaboration (Levine & Prietula, 2014), knowledge sharing (Levine & Prietula, 2006, 2012), and dynamic capabilities (Wollersheim & Heimeriks, 2016), among others. The test we present here is cautious: Even had we found that analytic skill and strategic intelligence are not held by individuals, they could still be held by organizations. But we found that these skills can be found in individuals, hence, they are likely to be found in organizations. Some valuable questions are how strategic intelligence can be learned, how it can reside in interpersonal routines (Winter, 1995) or stored in organizational memory (Walsh & Ungson, 1991), and how it can migrate from individuals to a team and an organization. We know that an organization can have routines that superimpose on the capabilities of most individuals in it. For instance, an organization can develop scripts for its sales forces that elevate the individual skills of any single sales person, say, by using prescribed responses to customers’ questions. Similarly, can an organization employ standard operating procedures to direct individuals to enhance their strategic intelligence, so that they could become better at anticipating competitor responses? If superior cognitive skills can reside in—or be enhanced by—an organization, this could be a strategic resource. Strategic intelligence may help explain a puzzle of strategy: Gibbons and Henderson (2013) show persistent performance differences between similar organizations. These performance differences are correlated with differences in managerial practices. Then, they ask: Why do best practices not diffuse more readily? Perhaps the answer has to do with differing cognitive skills. Strategic intelligence may put some managers ahead, but these skills cannot easily be transferred to others, or even understood (Polanyi, 1966). Some managers are ahead, but they Copyright © 2017 John Wiley & Sons, Ltd.

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do not know why. Others may know that they are behind, but they do not know what to do about it. In that sense, strategic intelligence may satisfy at least one of the tests for resources: It cannot be easily imitated, not at least until we learn how to teach it.

Acknowledgements S.S.L. acknowledges financial support from the European Research Council (695256) and the Hong Kong Research Council General Research Fund (14655416). R.N. thanks New York University for their hospitality and acknowledges financial support from the Spanish Ministry of Education, Generalitat de Catalunya, and Barcelona Graduate School of Economics (MINECO-ECO2011–25295 and ECO2014–56154-P). Author contributions: S.S.L. and M.B. designed the experiments, S.S.L. conducted them, M.B. and R N. analyzed the data, and S.S.L. wrote the article. For advice and encouragement, we thank Gary Bolton, Daniel C. Feiler, Richard Harrison, Ernan Haruvy, Ying Yi Hong, Elena Katok, Michael Leiblein, Zhiang (John) Lin, Felix Mauersberger, Mike Peng, and Victoria Prowse. We also thank audiences at the Center and Laboratory for Behavioral Operations and Economics at the University of Texas at Dallas, at the Israel Strategy Conference, and at meetings of the Strategic Management Society, the Academy of Management, the American Economic Association, and the Economic Science Association.

Resources This article has earned an Open Data badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available at http://doi.org/10.17605/OSF .IO/P49YA. Learn more about the Open Practices badges from the Center for Open Science: https:// osf.io/tvyxz/wiki.

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