A Prioritization of Competency Components of Operational Managers from Management Experts’View – A Case Study, Tehran, Iran
Bibliographic record
Abstract
The aim of present study is to study and to prioritize required competencies for appointing operational managers in Iran governmental organizations based on management professors and senior executives view. By considering data collecting method, this study is a descriptive-survey research and based on classification of purpose-based researches, it’s a developmental research and in terms of variables controlling and due to impossibility of variables controlling, this research is a pseudo-experimental research. The main information gathering tool was a researcher made questionnaire including 142 questions concerning competency components which was designed and edited by applying theoretical principles and frameworks. In order to make sure about validity of this questionnaire, an expert’s panel composed of management professors was applied. For testing its reliability, 30 questionnaires were completed and 95% Cronbach’s alpha was calculated which was an appropriate reliability coefficient for this study. Statistical population of this study was composed of all management professors in Tehran universities and also governor with at least three years of governing history and degree in master of management that by purposive or judgmental and snowball sampling methods, 70 management professors and 60 governors were selected as samples of this study. Data analysis of this study was done by the method of descriptive and inferential statistics and using factor analysis and Friedman’s ranking in Excel and SPSS software environments. The finding of this study reveals that competency components don’t have equal importance degree from two statistical population views.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".