Beyond the EVLN model: Quiet Quitting and the evolving dynamics of job dissatisfaction in human resource management
Bibliographic record
Abstract
This study examines ‘Quiet Quitting’ (QQ) as a novel and distinct response to job dissatisfaction, exploring its implications for Human Resource Management (HRM) and the evolving dynamics of employment relationships. Building upon Farrell’s Citation1983 Exit-Voice-Loyalty-Neglect (EVLN) model and integrating contemporary HRM theories, the research employs multi-dimensional scaling to better reflect current workplace behaviors. Using survey data from 238 workers across Canada, the USA, and the UK, the findings reveal that QQ mitigates individual dissatisfaction while subtly undermining organizational performance and employee engagement. Unlike traditional Exit and Neglect behaviors, QQ is less overtly disruptive and signifies a shift away from Loyalty and Voice. This evolution presents nuanced challenges for HR professionals, particularly in managing expectations, fostering engagement, and aligning HR practices with generational shifts in work-life balance and autonomy. Predominantly exhibited by Generation Z, QQ reflects changing workforce dynamics in the Western labour markets we study, emphasizing the need for innovative and contextually sensitive HR practices. By situating QQ within the broader HRM literature and offering practical recommendations, this study contributes to both HRM theory and practice, aiding organizations and policymakers in addressing generational trends in job dissatisfaction and workplace engagement.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".