Knowledge management and social entrepreneurship education: lessons learned from an exploratory two-country study
Bibliographic record
Abstract
Purpose Social entrepreneurship courses are among the fastest growing category of course offerings to entrepreneurship students (Brock and Kim, 2011) because both high growth potential- and steady growth-social ventures can create value and help solve social issues effectively and efficiently. As knowledge disseminators, entrepreneurship educators are in prime position to develop the knowledge, skills and abilities of students, which, in turn, increases their intentions to start a social venture and their ability to manage and grow their venture. Students gain an understanding about the role of entrepreneurship in addressing social opportunities, as well as knowledge related to starting, managing and growing social entrepreneurship ventures. This paper is divided into three parts. First, the authors broadly discuss the concept of social entrepreneurship. Second, the authors present an overview of the field of social entrepreneurship education (SEE) and its evolution. Finally, the authors supplement this review with an analytical examination of SEE, in which the authors present results of a cross-country analysis survey of over 200 entrepreneurship education programs in the USA and Canada. This paper aims to present information about: student enrollment in social entrepreneurship courses in comparison to other entrepreneurship courses; the frequency of offering social entrepreneurship courses and programs compared to other entrepreneurship courses and programs; and future trends in SEE. The results revealed a strong demand for social entrepreneurship from students, room for improvement in terms of the supply of course offerings, and a strong belief in the continued growth of social entrepreneurship. The authors conclude with suggestions about the future of SEE. Design/methodology/approach Analysis of secondary data derived from the oldest and most-frequently cited sources regarding entrepreneurship education in the USA and a novel data set examining entrepreneurship education in Canada. Both data sets were collected using an online self-report survey. Findings Demand for SEE continues to rise in both the USA and Canada. However, course and program offerings have not kept pace. Prominent trends in social entrepreneurship such as cross-campus programs and addressing the evolving demographics of students in higher education institutions need more attention. Originality/value A cross-cultural study of SEE that provides a high-level view of the state of the field today. In addition, the paper outlines the potential of the field of knowledge management for the future of SEE.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".