Depicting favorite organizational culture: An empirical case study
Bibliographic record
Abstract
The purpose of this study is to develop a model to depict favorite organizational culture. The research population consists of all Mapna's executive managers and the research sample includes 19 managers. In order to depict favorite organizational culture, in the first step, three approaches are applied and then the results of these three approaches are compared. In the first approach, Cameron and Quinn (1999) framework [Cameron, K. S., & Quinn, R. E. ( Diagnosing and changing organizational culture: Based on the competing values framework. John Wiley & Sons] and success indexes are used to determine favorite organizational culture. In the second approach, benchmarking technique is applied by implementing the Denison organizational culture database. In the third approach, based on competitive value framework (CVF), a questionnaire is developed and distributed among managers and the result is applied to depict favorite organizational culture. In order to analyze data, descriptive statistics are applied and the results indicate that all of these three approaches maintain the same results. Regarding to these approaches, one or all of these approaches are applicable for depicting favorite culture. Finally, the rate of gap between status quo and favorite organizational culture can be assessed and we can develop and implement plans for improving organizational culture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".