Organizational knowledge retention and knowledge loss
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper aims to examine the effectiveness of organizational information technology (IT)-based and non-IT-based knowledge transfer mechanisms (KTMs) for the retention of different types of knowledge from mobile experts. It differentiates among four types of knowledge loss (KL), namely, conscious knowledge (i.e. individual explicit knowledge that can be codified); codified knowledge (i.e. explicit knowledge captured at the social level); automatic knowledge (i.e. implicit individual knowledge); and collective knowledge (i.e. implicit knowledge embedded in the organization). Design/methodology/approach A research framework connecting the organizational knowledge retention (KR) cycle to KL is developed and an exploratory analysis is conducted using data from two case studies in the Canadian federal public service. Findings are confirmed using a third government agency. Findings Without the right processes in place for organizational knowledge retrieval and reuse, the KR cycle is not complete, leading to KL. The lack of available social KTMs for the conversion of individual to social objectified knowledge leads to KL. KTMs shortcomings increase the risk of automatic and objectified KL. Research limitations/implications Exploratory results demonstrate that KL does not always equate to lack of KR. Implementing knowledge-specific organizational KTMs is important to encourage the retention of individual knowledge at the social level. Propositions and a framework are developed for future research. Practical implications Mobile experts hold valuable knowledge at high risk of being lost by organizations. This paper provides managers with a set of guidelines to develop a knowledge-specific strategy focused on KTMs that increase KR and mitigate KL. Originality/value This paper challenges the assumption that KL only results from poor retention and studies both retention and loss to identify additional types of unintentional loss that occur when individual knowledge is not converted to social knowledge.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it