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The study of entrepreneurship in the twentieth century can be characterized as the import era: psychological trait theories (Brockhaus & Horowitz, 1986; McClelland, 1965; Powell & Bimmerle, 1980); psychological cognition theories (Busenitz, 1999; Katz, 1992); strategy theories (McDougall & Robinson, 1990; Sandberg, 1992); finance theories (Brophy & Shulman, 1992; McMahon & Stanger, 1995); marketing theories (Hills, 1981); population ecology theories (Aldrich, 1990); sociological network theories (Aldrich & Zimmer, 1986; Birley, 1985) and creativity theories (Long & McMullen, 1984) among others were imported to shape the development of entrepreneurship as an academic discipline. Thus far, the results of this prodigious effort are ambiguous at best; findings warrant continued effort in each stream but have to produce consequential insights into the nature, process, and dynamics of entrepreneurship. Why this may be so will be considered later. However, such a track record should prompt considerable reluctance to heed McMullen and Shepherd’s (2003) suggestion to import yet another theory – signal detection – rather than approach the study of entrepreneurship directly. Based on their exposition, such reluctance would be well founded except for the rather exciting fact that the framework has the potential for consequential insights if applied in another way. The purpose of this commentary is to critically evaluate the authors’ conceptualization and outline the alternative.

Gaglio’s work on opportunity recognition (Gaglio, 1997; Gaglio & Katz, 2001) represents an important contribution to the literature and has generated considerable scholarly attention. Therefore, it is with great pleasure that we respond to her commentary on our recent chapter (McMullen & Shepherd, 2003). Central to Gaglio’s commentary is a discussion about the appropriateness of our critique of the literature and a proposed alternate use for signal detection theory in building entrepreneurship theory. Responding to this commentary provides us the opportunity to better articulate our main arguments and to build on Gaglio’s ideas for an alternative application of signal detection theory.

Shane and Venkataraman (2000) suggest “the field [of entrepreneurship] involves the study of sources of opportunities; the processes of discovery, evaluation, and exploitation of opportunities; and the set of individuals who discover, evaluate, and exploit them” (p. 218). However, the study of the judgment required for opportunity evaluation has been greatly overshadowed by interest in opportunity recognition and to a lesser extent opportunity exploitation. This is surprising considering the number of economic theories of the entrepreneur that recognize sound judgment as a principal quality of entrepreneurship (Cantillon, 1755; Kirzner, 1973; Knight, 1921; Mises, 1949; Say, 1840; Schumpeter, 1934; Shackle, 1955). In fact, the first recognized theory of the entrepreneur defined the entrepreneur as someone who exercises business judgment in the face of uncertainty (Cantillon, 1755/1931, pp. 47–49). Similarly, Knight (1921, p. 271) suggests that the essence of entrepreneurship is judgment, born of uncertainty, and argues that it is this judgment that delineates the function of entrepreneur from that of manager. He goes on to point out that the function of manager does not in itself imply entrepreneurship but that a manager becomes an entrepreneur when he exercises judgment involving liability to error (Knight, 1921, p. 97). However, the judgment referred to by these theorists is not just any form of judgment, it is judgment exercised in the decision of whether to take action.

In his soliloquy, Hamlet reasons about the choice of whether to end his life or not. Our own experience of choice contains many such instances of choice as the outcome of a reasoning process. This aspect of choice is not discussed in this book although I would look to Chapter 3 on mathematics, logic, artificial intelligence and ordinary language to provide a route into investigating this aspect.

We explore how, and how accurately, people assess their influence over others’ behavior and attitudes. We describe the process by which a person would determine whether he or she was responsible for changing someone else’s behavior or attitude, and the perceptual, motivational, and cognitive factors that are likely to impact whether an influencer’s claims of responsibility are excessive, insufficient, or accurate.

We first review classic work on social influence, responsibility or blame attribution, and perceptions of control, identifying a gap in the literature with respect to understanding how people judge their own responsibility for other people’s behavior and attitudes. We then draw from a wide range of social psychological research to propose a model of how an individual would determine his or her degree of responsibility for someone else’s behavior or attitude.

A potential influencer’s beliefs about the extent of his or her influence can determine whether he or she engages in an influence attempt, how he or she engages in such an attempt, and whether he or she takes responsibility for another person’s behavior or beliefs.

For decades, scholars researching social influence have explored how one’s behavior and attitudes are shaped by one’s social environment. However, amidst this focus on the perspective of the target of social influence, the perspective of the influencer has been ignored. This paper addresses the largely neglected question of how much responsibility influencers take for the impact their words, actions, and presence have on others.

In this chapter, we overview different neuroenhancement techniques that could be applied for accelerating the learning process in a number of tasks that are associated with occupational roles. The techniques range from: (1) pharmaceutical and invasive methods with limited applicability to the healthy population, due to possible side effects and obtrusiveness; (2) game-based brain training that shows task-specific potential, but may not generalize; and (3) a promising new research direction in which the goal is to “train” the brain to reach an optimal cognitive state for performing a given task, and remain in this state by self-regulation. However, in order to accomplish this goal of brain training, the neurological markers that best discriminate good task performance need to be identified. We also review a number of initial studies in this chapter which have analyzed such markers in a variety of training-related applications for different occupations, such as military/security (e.g., marksmanship, deadly force judgment and decision making, submarine piloting and navigation, phishing detection), medicine (e.g., robot-assisted surgery), banking (e.g., financial traders), sports (e.g., golf, archery, and baseball), or entertainment (e.g., musicians and actors). The promising results of these early studies are fueling interest in neuroscience-based technology and methods in the rapidly developing field of organizational neuroscience (e.g., leadership research). We conclude the chapter with a discussion of future research directions.

Experts respond to the same incentives as people in other areas of human action, and in the same ways. This insight is a truism: Experts are ordinary people, not otherworld creatures. The disciplined pursuit of this common sense observation helps us to reach conclusions about experts that might be surprising or counterintuitive.