The application of a selection of decision-making techniques by employees in a transport work environment in conjunction with their perceived decision-making success and practice
Bibliographic record
Abstract
A lack of optimum selection and application of decision-making techniques, in conjunction with suitable decision-making practice and perception of employees in a transport work environment demands attention to improve overall performance. Although multiple decision-making techniques exist, five prevalent techniques were considered in this article, namely the Kepner-Tregoe, Delphi, stepladder, nominal group and brainstorming techniques. A descriptive research design was followed, using an empirical survey which was conducted among 210 workers employed in a transport work environment and studying in the field of transport management. The purpose was to establish to what extent the five decision-making techniques are used in their work environment and furthermore how the decision-making practice of using gut-feel and/or a step-by-step decision-making process and their perception of their decision-making success relate. The research confirmed that the use of decision-making techniques is correlated to perceived decision-making success. Furthermore, the Kepner-Tregoe, stepladder, Delphi and brainstorming techniques are associated with a step-by-step decision-making process. No significant association was confirmed between the use of gut-feel and decision-making techniques. Brainstorming was found to be the technique most frequently used by transport employees; however, it has limitations as a comprehensive decision-making technique. Employees working in a transport work environment need training in order to select and use the four comprehensive decision-making techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".