Toward rigor and parsimony: a primary validation of Kolvereid’s (1996) entrepreneurial attitudes scales
Bibliographic record
Abstract
Questioning the validity of scholarly work is not a typical path to publication in the management field. However, although considerable scholarship assesses entrepreneurial attitudes and intentions models of behaviour, methodological weaknesses in scale development have hampered scholars’ ability to rigorously interpret and build upon their research findings. We review 20 years of research and discover that the pioneer measure of entrepreneurial attitudes as a predictor of self-employment intentions, has yet to be empirically validated. We show that construct and measurement differences, one-off modifications to existing scales and a lack of adequate justification may partially explain why studies in the entrepreneurship education domain have produced inconsistent results. We address this limitation by performing factor analytic techniques on data from two sets of English-speaking university students from two North American countries. The result is a more parsimonious and streamlined ‘mini-Kolvereid’ scale. We further demonstrate that this scale is an effective predictor of entrepreneurial intentions.
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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 | 0.239 | 0.498 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".