Deploying ‘Connectors’: A Control to Manage Employee Turnover Intentions?
Bibliographic record
Abstract
This paper investigates whether individuals that we identify as “connectors”—who possess a blend of innate traits and skills that predispose them to be personable, willing to relate to others, and able to influence others’ relationships—can serve as a catalyst for improving group outcomes. More specifically, we explore whether identifying connectors and placing them in work groups can serve as a control to help firms manage undesirable voluntary employee turnover by improving the group experience and reducing their fellow group members’ turnover intentions. We conduct an experiment to test our hypotheses that members in a group with a connector (versus without) have lower turnover intentions because their experiences are perceived as more positive, and that this turnover intention effect is more pronounced for group members who are demographically distinct from others in their group. Results are consistent with predictions, although the effect of connectors on lowering group members’ turnover intentions is driven by members who are distinct. Our findings broaden the understanding of who connectors are and how they affect group interactions, and further suggest that hiring and deploying connectors in work groups can be an effective component of a more comprehensive retention strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".