Contingent workers' impact on standard employee withdrawal behaviors: Does what you use them for matter?
Bibliographic record
Abstract
Abstract Previous research has suggested that workforce mixing—simultaneously using contingent workers and standard employees—can negatively affect standard employee attitudes and behaviors. In this study, we consider the impact of two reasons employers choose to use contingent workers (to enhance standard employee employment stability and to reduce labor costs) on standard employee withdrawal behaviors (absenteeism and turnover). We posit that when the aim of using contingent labor is to enhance standard employee employment stability (employment stability contingent labor strategy or ESCLS), the effects on standard employee withdrawal behaviors will differ from when the aim is to reduce labor costs (labor cost contingent labor strategy, or LCCLS). Using a sample of 90 firms that employ a mixed workforce, we examine the influence of ESCLS, LCCLS, and high investment HR systems (HIHRS) on standard employee withdrawal behaviors at the firm level. In addition to supporting the hypothesized direct (positive) effect of LCCLS on standard employee withdrawal behaviors, this study's results support the hypothesized moderating effects of HIHRS on the negative relationship between ESCLS and standard employee withdrawal behaviors and the positive relationship between LCCLS and standard employee withdrawal behaviors. Implications for research and practice and suggestions for further research are discussed. © 2010 Wiley Periodicals, Inc.
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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 | 0.004 | 0.018 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".