Supplier Diversity Certification Success Factors: Survey of Women-owned Suppliers
Bibliographic record
Abstract
This article develops and tests theory-driven hypotheses on the impact of supplier diversity certification for women-owned businesses.Supplier diversity programs create opportunities for businesses majority-owned, managed, and/or operated by Indigenous Peoples, people with disabilities, veterans, visible minorities, 2SLGBTQI+ people, and women to become suppliers of goods and services, typically to large corporate or government buyers.Drawing on contingency theory and the resource-based view (RBV), supplier size and duration of certification are hypothesized to impact success.Diversity certification is conceptualized as an intangible asset, i.e., as a valuable resource.A survey was developed to collect data on organizational size, duration of certification, business success and other relevant variables, such as motivators for and barriers to certification.While both size and duration appear to facilitate success, there is also evidence that duration mediates the size effect.The discussion includes implications for theory and practice.There is a clear need for future research to explore a possible combination of diversity certification and capability certification, across all communities of diversity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".