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Questions, Methods and Choices
Edited by Catherine Walshe and Sarah Brearley
Edited by Marta Sinclair
Edited by Marta Sinclair
Edited by Mellani Day, Mary C. Boardman and Norris F. Krueger
This chapter will: 1) systematically analyze the totality of existing published peer-reviewed articles up to date on entrepreneurship research using exclusively any of the available neuroscientific tools (for example, fMRI, EEG, MEG, and so on). Thus far a review as such as not been produced. This review is based on the whole ISI Web of Knowledge’s Social Sciences Citation Index to avoid a potential bias and/or omissions. The exclusion criteria applies to: articles that primarily focus on neuroscience, psychology or the like but not entrepreneurship; articles that are not peer reviewed; working and not empirical papers. 2) To uncover the main inconsistencies, knowledge gaps, conceptual and methodological problems in this field. 3) Propose a research agenda for coordinating efforts to move the field forward as well as to stress the potential of examining entrepreneurship phenomena with the aid of neuroscientific tools.
M.K. Ward, Crystal Reeck and William Becker
The purpose of this chapter is to support entrepreneurship scholars in their efforts to integrate neuroimaging with entrepreneurship. First, we describe the potential benefits of using neuroimaging in entrepreneurship research. Second, we discuss general issues and considerations to make in neuroimaging research. Then we focus on functional magnetic resonance imaging (fMRI) as a neuroimaging method to bring the neural level of analysis to entrepreneurship research. We discuss opportunity recognition and assessment, and entrepreneurial decision making as examples of topic areas ripe for neuroentrepreneurship and fMRI. We subsequently describe hypotheses designs and best practices in fMRI research. Finally, we close with a discussion regarding interpretation and communication of results.
Kelly G. Shaver, Leon Schjoedt, Angela Passarelli and Crystal Reeck
Do entrepreneurial ventures involve risk? The quick answer to this question is “well, of course they do!” And there is no shortage of supportive data. For example, the US Panel Studies of Entrepreneurial Dynamics (PSED) Gartner et al., 2004; Reynolds and Curtin, 2009, 2011) shows that as many as 72 months after a business-organizing venture begins, only some 30 percent of efforts have produced new firms. Spending six years trying to organize a company “risks” at least the time and opportunity cost; selling a company for less than the venture-capital raised “risks” the wealth of the investors. But there are at least two important differences. First, investors can take a portfolio approach to try to balance risks and rewards. Individuals, however, typically start only one enterprise at a time, so they stand to lose “all,” not just “some.” Second, investment decisions made by angels or venture capitalists are typically collective decisions, involving the best guesses of multiple brains. By contract, business-organizing decisions are typically made by a single brain. At the present stage of theory and research, the single-brain decisions have been most heavily studied by the methods of neuroscience. Consequently, this chapter restricts its focus to the judgments of risk made by individual entrepreneurs. When the goal is to examine the brain correlates of variations in risk judgments, restricting the investigation to an individual person is merely the beginning. At least four other design elements need to be considered for the final answers to be clear: (1) there must be a conceptual analysis that distinguishes one sort of risk from others; (2) the research designs chosen must maximize the opportunity to obtain meaningful results; (3) the concepts selected for testing must be operationalized unambiguously; (4) potential methodological confounding must be avoided. The present chapter is organized to address each of these concerns in turn to identify practices that might better enable researchers to understand the cognitive neuroscience of entrepreneurial risk.
Mellani Day and Mary C. Boardman
Scholars have discussed both the profit motive (Simons and Astebro, 2010) and a broader set of motives (Elkington, 1997; Becker, 1993; Balog et al., 2014) when studying entrepreneurial behavior. Some of these are internal and underlie how entrepreneurs perceive and experience risk and reward, costs and benefits. These motivators that may affect an internal return on investment (ROI) calculation can also be examined through a neuroentrepreneurship lens. Day (2014) takes a first step in this through framing the issue, identifying a set of potential costs and benefits, and surveying an entrepreneur to develop a hypothesis for how these may be ranked in order of importance. As with any theory or model presented, the next step is to empirically test and refine the model. In this chapter we present a first step in the empirical testing of the ROI model presented in Day (2014). We identify existing data that measures these costs and benefits to varying degrees, then present results from our preliminary regression analysis and hypothesis testing. Then we discuss the future research necessary to build upon this moving forward, and providing specific suggestions for corresponding neuroexperimental research designs.
This chapter builds upon the presumption that a fusion between entrepreneurship and neuroscience is justified by the methodological and technological advantages facilitated by the former. It first suggests that the joint use of neuroscience tools such as laboratory experiments and brain-driven technologies are propitious to entrepreneurship research because they allow a deeper level of analysis: the entrepreneurial brain. Second, focusing on one of these tools – laboratory experiments – the chapter unveils nine principles to take into account for the good design of an experiment within the structure of a brain-driven entrepreneurship study. It is normal that unraveling these principles are to assist entrepreneurship researchers with gaining a further understanding of the experimental design fundamentals when adopting a neuroscience perspective, while assisting them to moderate the unavoidable challenges ingrained in this interdisciplinary crusade.
What if we use what we know about brain plasticity and adopted the frame of mind that entrepreneurship can be learned? Instead of conducting research based principally on past data sets, we could gather, analyze, and interpret data in real time. Not only would this give us an enhanced understanding of the neural correlates to traits common to entrepreneurs, helping us to define better the ever-nebulous “entrepreneurial” mindset, but it would also provide important clues into the effectiveness of entrepreneurial education.