Distinguishing entrepreneurial approaches to opportunity perception

Purpose – Whether opportunities are discovered or created by entrepreneurs is a foundational question in entrepreneurship research. The purpose of this paper is to examine women entrepreneurs in high-growth new ventures and explore the cognitive resources that distinguish between three approaches to opportunity perception: opportunity discovery; opportunity creation; and a combined discover-create (ambidextrous) approach. Design/methodology/approach – Using questionnaire responses from 165 women entrepreneurs in highgrowth new ventures, K-means clustering was used to determine three approaches to opportunity perception. The cognitive resources associated with each approach were then identified using multiple discriminant analysis. Finally, multivariate analysis of variance was conducted to examine the relationship between opportunity perception and growth expectations. Findings – These results demonstrate different approaches to opportunity perception among entrepreneurs in high-growth new ventures, the cognitive resources that reinforce each approach, and the expected new venture growth outcomes. Research limitations/implications – The findings offer insight on the cognitive origins of opportunity perception by empirically identifying distinct approaches to opportunity perception and the cognitive resources that underlie each. The study relies on a unique sample of entrepreneurs to understand complex cognitive phenomenon. Practical implications – Understanding the effects that cognitive factors have on opportunity perception provides direction for current and aspiring entrepreneurs. The findings and instrument may be used for professional development and to inform educational strategies. Originality/value – The findings offer important contributions to entrepreneurial theory and practice by addressing repeated calls for research that examines the cognitive antecedents enabling opportunity formation (discovery, creation or both). This manuscript empirically does so, while opening up possibilities for future research

Entrepreneurship Assessment in Higher Education: A Research Review for Engineering Education Researchers

BackgroundDespite the wide adoption of entrepreneurship by United States engineering programs, there have been few advances in how to measure the influences of entrepreneurial education on engineering students. We believe the inadequate growth in engineering entrepreneurship assessment research is due to the limited use of research emerging from the broader entrepreneurship education assessment community.PurposeThis paper explores entrepreneurship education assessment by documenting the current state of the research and identifying the theories, variables, and research designs most commonly used by the broader community. We then examine if and how these theories and constructs are used in engineering entrepreneurship education.Scope/MethodTwo literature databases, Scopus® and Proquest, were searched systematically for entrepreneurship education assessment research literature. This search yielded 2,841 unique papers. Once inclusion and exclusion criteria were applied, 359 empirical research papers were coded for study design, theory, variables measured, instruments, and validity and reliability.ConclusionsWhile there has been growth in entrepreneurship education assessment research, little exchange of ideas across the disciplines of business, engineering, and education is occurring. Nonempirical descriptions of programs outweigh empirical research, and these empirical studies focus on affective, rather than cognitive or behavioral, outcomes. This pattern within the larger entrepreneurship community is mirrored in engineering where the use of theoryâ based, validated entrepreneurship education assessment instruments generally focuses on the context of intent to start a new company. Given the engineering community’s goals to support engineering entrepreneurship beyond business creation, the engineering education community should consider developing assessment instruments based in theory and focused on engineeringâ specific entrepreneurship outcomes.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145556/1/jee20197.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145556/2/jee20197_am.pd

A framework for evolutionary systems biology

<p>Abstract</p> <p>Background</p> <p>Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.</p> <p>Results</p> <p>Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions <it>in silico</it>. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.</p> <p>Conclusion</p> <p>EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.</p