Double shock effects of downsizing in economic downturn on employees with high firm-specificity
Bibliographic record
Abstract
Extant human capital theory relies on isolating mechanisms through which the employer restricts mobility of employees with firm-specific human capital (less favorable to external market). Recently, studies suggested a possibility that such employees experience reduced job satisfaction and increased attention from new employers. In an effort to find a strong situation in which collapse of isolating mechanisms happens prevalently, this paper suggests double shock effects of downsizing in economic downturn, which cause employees with firm-specific skills to feel violation of the psychological contract and want to leave the organization voluntarily, using two Korean labor panel studies. Study 1 suggested that employees with firm-specific skills experience were less satisfied with their jobs in economic downturn than those with general skills. Study 2 showed that downsizing cause employees to perceive low levels of trust in talent management and intend to leave the organization when the employer downsize their employees in economic downturn. Firm-specificity worked as a moderator between trust and turnover intention in a way that employees with high firm- specificity are more likely to feel distrust in talent management than those with low firm-specificity, resulting in high levels of turnover intention. This paper contributes to human capital literature by shedding a light on the possibility that traditional belief of human capital theory could be inconsistent with a specific situation and fail to explain unexpected outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".