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 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 | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".