Managers and organizational forgetting: a synthesis
Bibliographic record
Abstract
Purpose This paper aims to evaluate how managers influence accidental and intentional organizational forgetting, i.e. knowledge depreciation, knowledge loss and unlearning. Design/methodology/approach The literature was reviewed based on predetermined search terms to identify peer-reviewed articles published in English and available in full-text format from the EBSCOhost and Google Scholar databases. Empirical and theoretical contributions were included. Additional articles, books and book chapters were manually selected and included based on recent reviews and syntheses of organizational forgetting work. Findings Findings revealed that managers contributed to preventing accidental knowledge depreciation and loss and preserving organizational memory. With respect to intentional forgetting, findings revealed contradictory positions: on the one hand, managers contributed to the disbandment of existing beliefs and frames of reference, but on the other hand, they preserved existing knowledge and power structures. Research limitations/implications The study was limited by the accessibility of subscribed journals and databases, research scope and time span. Practical implications This paper provides useful guidelines to managers who need to reduce the disruptive effects of accidental forgetting or plan intentional forgetting, i.e. managed unlearning. Originality/value This paper represents a first attempt to review and define the influence of managers on organizational forgetting.
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 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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".