The people make the process: commitment to employees, decision making, and performance
Bibliographic record
Abstract
This study argues that a well designed decision making process will have its most positive impact on company financial performance when it is carried out by a capable, motivated and dedicated workforce. Prior research has determined that such a workforce can be developed via an organization’s commitment to its employees (OCE) in the form of ample training and compensation, fairness, and meaningful personal consideration. We argue that OCE will enhance financial performance where it is able to improve the quality of a decision making process that emphasizes ample information processing, collaboration, and initiative. Conversely, these three dimensions of decision making are expected to be of little value where OCE—and hence a capable and motivated workforce—are lacking. These expectations were borne out in our study of Korean companies. Specifically, we found positive associations between return on assets and the interactions between OCE and information processing, collaboration, and initiative, respectively. We found also that these interactions contributed the most to return on assets in uncertain environments, where effective information processing, collaboration and initiative were especially important.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".