Identifying different methods for creating knowledge from lessons learned in project oriented organizations
Bibliographic record
Abstract
Nowadays, the increase in competition has increased the relative importance of innovation for most firms and many managers believe a good innovation must be knowledge oriented. This paper has tried to determine different methods for creating knowledge in project oriented organizations. The study designs a questionnaire in Likert scale and distributes it among 32 experts who were well informed about different methods of knowledge creation and lessons learned. Cronbach alphas for all components of the survey were well above the desirable level. The study has detected 11 methods for knowledge creation and lessons learned. In terms of preliminary assessment, business transactions has received the highest impact while knowledge team has received the highest effect in terms of necessary assessment. The results of this survey have indicated that although there are several methods for detecting knowledge within organizations, in most cases, it is not easy to gain value added knowledge within an organization, quickly. The people who participated in our survey have indicated that organizational commitment, brainstorming, Delphi and storytelling also have played important role for creation of knowledge. The results have also shown that brainstorming, knowledge brokers, map knowledge and work experience were easier to use for knowledge creation and lessons learned compared with other forms of knowledge creation.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".