The human capital management perspective on quiet quitting: recommendations for employees, managers, and national policymakers
Bibliographic record
Abstract
Purpose The purpose of this Real Impact Viewpoint Article is to analyze the quiet quitting phenomenon from the human capital management perspective. Design/methodology/approach The methods comprise the analysis of 672 TikTok comments, the use of secondary data and literature review. Findings Quiet quitting is a mindset in which employees deliberately limit work activities to their job description, meet yet not exceed the preestablished expectations, never volunteer for additional tasks and do all this to merely maintain their current employment status while prioritizing their well-being over organizational goals. Employees quiet quit due to poor extrinsic motivation, burnout and grudges against their managers or organizations. Quiet quitting is a double-edged sword: while it helps workers avoid burnout, engaging in this behavior may jeopardize their professional careers. Though the term is new, the ideas behind quiet quitting are not and go back decades. Practical implications Employees engaged in quiet quitting should become more efficient, avoid burnout, prepare for termination or resignation and manage future career difficulties. In response to quiet quitting, human capital managers should invest in knowledge sharing, capture the knowledge of potential quiet quitters, think twice before terminating them, conduct a knowledge audit, focus on high performers, introduce burnout management programs, promote interactional justice between managers and subordinates and fairly compensate for “going above and beyond.” Policymakers should prevent national human capital depletion, promote work-life balance as a national core value, fund employee mental health support and invest in employee efficiency innovation. Originality/value This Real Impact Viewpoint Article analyzes quiet quitting from the human capital management perspective.
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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 | 0.034 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.017 | 0.034 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".