A Comprehensive Framework for Entrepreneurship Education
Bibliographic record
Abstract
ABSTRACTWe report on application of a novel and comprehensive framework for assessment and development of entrepreneurship education programs. By building on models available in current literature, and by incorporating key constructs from Theory of Planned Behaviour and from Stakeholder Theory, we present a framework that places design of an entrepreneurship education program into a broad context where decisions of program content and pedagogy are driven by considerations of role of program in social, economic, and institutional environment. This situating of design into a broader context results in programs that are more responsive to environmental circumstances and stakeholder needs and aspirations, and that exhibit greater coherence in performance measurement. We illustrate application of this framework through a review of design of two entrepreneurship programs, one in Canada and one in Germany.INTRODUCTIONNew venture creation is a vital contributor to a healthy economy. New ventures contribute to economic growth by commercializing innovation, bringing forth novel resource combinations, and driving production of markets through new competitive pressures (Wong, Ho, & Autio, 2005). The entrepreneurial actions of new ventures leads to discovery (Webb, Ireland, Hitt, Kistruck, & Tihanyi, 2011) and increased productivity (Martin & Osberg, 2007), and thence to broad social and economic progress (Wennekers & Thurik, 1999). Policymakers in most Western countries therefore believe that greater levels of entrepreneurship are required to reach higher levels of economic growth and innovation (Oosterbeek, van Praag, & Ijsselstein, 2010; Williams, Balaz, & Wallace, 2004). Donald Kuratko called entrepreneurship the most potent economic force world has ever experienced (2005: 577). Despite this, levels of entrepreneurship remain uneven across countries, societies, and communities (Xavier, Kelley, Kew, Herrington, & Vorderwulbecke, 2012).In response to perceived need for more entrepreneurs, there has been a dramatic rise in entrepreneurship education at a post-secondary levels (Kuratko, 2005; Mars & Rios-Aguilar, 2010; Solomon, 2007; Vesper & Gartner, 1997). Chamey and Libecap (2000) suggest that reasons for such sudden growth of entrepreneurship education at universities are economic shifts, a growing demand from students, an increase in funding for entrepreneurship education, and a desire to increase technology transfer and innovation generation at post-secondary level. The benefits of entrepreneurship education include positive student benefits and economic outcomes (Chamey & Libecap, 2000), and significant institutional benefits (Finnila, 2006). These benefits result in increased innovation, increased new venture creation, a propensity for selfemployment, higher annual incomes, and higher job satisfaction.The rise in demand for post-secondary entrepreneurship education has led to emergence of new programs at existing educational institutions, expansion and renewal of existing programs, and creation of entirely new institutions dedicated to entrepreneurship (e.g., Founders Institute). Yet it is not clear that these efforts towards education are very effective. For instance, a recent paper by Rideout and Gray (2013) reviewed a decade's worth of empirical studies on topic and found majority of entrepreneurship education programs lacked methodologies robust enough to yield reliable results. And some studies have even found evidence that effects of entrepreneurship education can even be negative (Nabi, Holden, & Walmsley, 2010; Oosterbeek, et al, 2010; von Graevenitz, Harhoff, & Weber, 2010).There has been surprisingly little research on performance of entrepreneurship education programs. Relatively few studies have examined influence of such programs on student attitudes, goals, competence, and behaviours (Duval-Couetil, 2013; Peterman & Kennedy, 2003). …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".