Strategies to Implement the Baldrige Criteria for Performance Excellence
Bibliographic record
Abstract
Only a small number of U.S. businesses have adopted the Baldrige Performance Excellence Program. The purpose of this multiple case study was to explore strategies that executive business leaders use to implement the Baldrige Criteria for Performance Excellence. The study population consisted of six business executives and two organizations in the U.S. state of Texas, all with experience in implementing the Baldrige Criteria for Performance Excellence. The theory of high performance work systems provided the conceptual framework for the study. Data were gathered from interviews and record reviews that were conducted within the organizations. Inductive analysis was used to identify words, phrases, ideas, and actions that were consistent among participants and organizations as well as patterns and themes. Triangulation of sources between the interview and record review data was used for consistency. Three main themes emerged from data analysis: organizations embedded the Baldrige Criteria for Performance Excellence into native work models; they also used robust strategy deployment systems with accountability for action plans to spread the Baldrige Criteria for Performance Excellence; and, rather than specifically create goals to align with the Baldrige Criteria for Performance Excellence, they identified actions to reach organizational strategic goals that were molded using the Baldrige Criteria for Performance Excellence as a business model.
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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 | 0.052 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".