The Great Resignation: the great knowledge exodus or the onset of the Great Knowledge Revolution?
Bibliographic record
Abstract
Purpose The purpose of this Real Impact Viewpoint Article is to analyze the phenomenon of the Great Resignation from the knowledge management perspective. Design/methodology/approach It applies the knowledge-based view of the firm to the notion of the Great Resignation, reviews the extant literature and relies on secondary data. Findings The Great Resignation has created numerous knowledge-related impacts on the individual, organizational and national levels. On the individual level, because of an accelerating adoption of freelancing, the future may witness an expansion of the category of the knowledge worker and a growing need for personal knowledge management methods and information technologies. Organizational effects include knowledge loss, reduced business process efficiency, damaged intra-organizational knowledge flows, lower relational capital, lost informal friendship networks, difficulty attracting the best human capital, undermined knowledge transfer processes and knowledge leakage to competition. Countries may also witness the depletion of national human capital. Practical implications Managers should learn how to use the available human capital more efficiently; realize the importance of universal succession planning programs; automate knowledge-centric business processes; facilitate knowledge-based IT initiatives by implementing self-functioning virtual communities, including enterprise social networks; restructure organizations to optimize intra-organizational knowledge flows; adjust strategies, products and target markets based on the available human capital; and create telecommuting conditions for people with disabilities who cannot be physically present. Knowledge management scholars are presented with a unique opportunity to convert the numerous theoretical insights accumulated within the boundaries of their discipline into practical application to facilitate the Great Knowledge Revolution. Originality/value This viewpoint offers managerial recommendations and inspires future Great Resignation investigations.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".