Planning the Decision Making Process: A Multiple Case Study
Bibliographic record
Abstract
The decision-making process involves making decisions about the decision process itself. Understanding better about “how to decide” decision makers can improve the quality of their decisions and using less time and resources. A multiple case study was developed to identify factors that may lead a decision-making process to be planned or unplanned. In the three cases studied we observed the planning of the decision-making process, however, with distinct degrees of effort and the time frame of the problem’s occurrence and the decision-making. We identified five main factors that influence the planning of the decision-making process: i) the nature of the problem—whether the problem is new or recurrent to the firm, ii) awareness regarding the problem, the objectives and alternatives, iii) decision maker’s experience, iv) organizational culture regarding risk taking in decision making, v) decision maker’s autonomy level and holistic view of the firm and the conjuncture embedded. By studying the decision planning process of these three cases we believe we could draw attention to a perspective of the decision process seldom studied and open the possibility of new studies involving the decisions about the decision process—the meta-decisions.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".