Teaming up with temps: the impact of temporary workers on team social networks and effectiveness
Bibliographic record
Abstract
Temporary workers offer immediate benefits to the bottom line; yet, it is unclear how incorporating temporary workers into teams affects how they function. We apply social identity theory to propose that temporary workers significantly reduce individual- and team-level networks and team effectiveness but that commitment to the leader and intergroup competition can help temporary and permanent employees work together more effectively. Using a sample of employees nested in teams (Study 1, n = 312), we found that status differences affected member interactions resulting in sparser advice and friendship networks for temporary workers compared to their permanent counterparts. At the team level (Study 2, n = 58), these team member differences or contract diversity impacted team functioning through advice networks, such that, teams with greater contract diversity had sparser networks and were less effective. Further, commitment to the leader was found to moderate the negative impact of contract diversity on advice and friendship network density. With the increasing use of temporary worker and the prevalent use of teams, these findings have broader implications for HR functions and present possible avenues to mitigate the negative consequences of temporary workers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".