Turnover experiences in public accounting and alumni's decisions to “give back”
Bibliographic record
Abstract
Abstract This study examines turnover experiences in public accounting, including the exit phase (from public accountants' initial thoughts of leaving to their exit) and the post‐exit phase (from their exit to the present moment) of the turnover process. Drawing on social exchange theory and organizational support theory, we also investigate the relationship between these phases by exploring how turnover characteristics within the exit phase impact alumni's decisions to engage in post‐employment citizenship in the post‐exit phase (e.g., recommending the firm's services to others). Using the experiential questionnaire method, we rely upon two separate surveys to investigate the turnover process from the perspective of 284 firm alumni (“leavers”) and 83 experienced public accountants (“stayers”). Our process‐based research method allows us to gather a large and rich data set that provides multiple perspectives on the turnover experience in public accounting. Our results not only provide insights into the underlying factors influencing turnover but also indicate several places in the turnover decision process where firms can strategically intervene. Finally, our results show that several turnover characteristics within the exit phase impact post‐employment citizenship behaviors in the post‐exit phase. Consequently, our results demonstrate that the characteristics that drive employees' decisions to leave the firm also play a significant role in shaping their post‐employment citizenship behaviors following their departure.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".