The Impact of Organizational Forgetting on Knowledge Management: Evidence from Pharmaceutical Industry in Egypt
Bibliographic record
Abstract
Purpose: The purposes of this paper are to determine the impact of Organizational Forgetting (OF) on Knowledge Management (KM) among employees at the Pharmaceutical industry in Egypt.Design/methodology/approach: Present study is conducted by descriptive-survey method and its population consists of employees at the Pharmaceutical industry in Egypt. 356 standard questionnaires were distributed of which 285 questionnaires (80%) were returned. To gather data, KM questionnaire devised by Jakob (2003) and Wiig (2003) and OF questionnaire devised by Fernandez & Sun, (2009) and Moshabbeki et al., (2012) are used.Findings: The research confirmed a conceptual model for OF. Moreover, research results showed that there is a meaningful relationship between OF and KM. Research results also indicate that OF impacts on KM.Research limitations/implications: Managers should encourage their employees to share their knowledge. Organizational knowledge can be created through individuals’ interactions. This study has some limitations. First, this paper just focuses on organizations to find new perspective for the OF literature. Second, because of the scope of this research, interviewees are limited to individuals who have knowledge or take any seminars related to field of this sector. Other sectors must be considered to attain detailed knowledge related to OF because case-specific studies will bring new dimensions to the literature of OF.Originality/value: First, this study makes a research contribution to the field of OF because studies related to OF mostly consist of conceptual papers. Second, I have introduced two new perspective to the concept of OF through this research paper.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".