ABSTRACT
The nature of entrepreneurship is complex and nonlinear per se (Alvarez and Busenitz 2001: 768). No matter which school of entrepreneurship is chosen for reference, the fundamental definition of entrepreneurship as the exploitation of opportunities to accomplish original and value-generating innovations implies tremendous complexity. For instance, taking a resource-oriented perspective on entrepreneurship (Volery 2005 based on Penrose 1959) means understanding the entrepreneurial process as an allocation of input factors (resources) in an original way (Alvarez and Busenitz 2001). Therefore, the entrepreneur needs to recognize an opportunity (Shane and Venkataraman 2000 see also Murphy et al. 2005), get access to required resources (Brush et al. 2001) and organize these resources within a firm (Alvarez and Busenitz 2001) to create original and valuable new combinations. According to the resource-based theory, the entrepreneur has to address several dimensions to cover competitive advantages with his resourcecombination on a long-term basis (Alvarez and Busenitz 2001 based on Peteraf 1993). For instance, to prevent his once reached competitive advantage the entrepreneur needs to develop inimitable resources (Itami 1987) like social networks (Barney 1995) or distinctive knowledge (Barney 1991, Grant and BadenFuller 1995) to ensure sustainable heterogeneity. This multi-dimensional solution space for strategic decisions and development directions is a highly complex environment with nonlinear causalities which makes it difficult or even impossible to predict certain outcomes, or behave according to standardized patterns. Not only the nature of entrepreneurship in general, but also the specific characteristics of scientific entrepreneurship make this field even more complex. We understand scientific entrepreneurship as the recognition of business or social opportunities and their exploitation from within an academic, knowledge-intensive
environment to carry out highly original and valuable combinations and put them through in terms of innovations. This definition reveals three main causes for complexity, which are (a) the characteristics of the academic environment; (b) the kind of actors/promoters; and (c) the characteristics of the innovations themselves. First, the academic environment is complex per se. Complexity refers to the traditional mission of universities as sources of knowledge creation and dissemination (Bok 2003, Geisler 1993). The creation of new knowledge implies nonlinear causalities, which are a driver for complexity (Higgings 2006: 191ff.). Second, the actors within the academic community are extensively different in nature, and also with regard to their different roles: researchers, educators, learners, co-researchers, etc. For scientific entrepreneurship, this implies that there is a very heterogeneous group of addressees with different expectations, who not only have to be served with appropriate measures for each subgroup (Magin and von Kortzfleisch 2008: 3), but also should be matched with one another to create entrepreneurial teams with multiple experiences and capabilities. Third, scientific entrepreneurship often leads to innovative knowledgeintensive and technological innovations (Kulicke 2006) which are likely distinctive due to their originality, and whose exploitation is even more complex because of their uniqueness (Kohn and Spengler 2007: 9). Taking all the case studies of entrepreneurial ventures with roots in academic institutions (see e.g. O’Shea et al. 2007 for the MIT Case), there is a tremendous portion of innovations carried out by those ventures, for which e.g. a market or even a demand did not exist before.
