A Meta-Analytic Structural Model of Self-Monitoring, Interpersonal Effectiveness, and Status at Work
Bibliographic record
Abstract
Gangestad and Snyder (2000) theorized that concerns for status are the motivational foundation of self-monitoring and that its self-presentational behavior is designed to cultivate social status. We present the most comprehensive meta-analytic test of associations between self-monitoring and status at work. Overall, results confirm theory (grand mean ? = .22), suggesting that desiring and acquiring status are defining qualities of self-monitoring. Next, drawing on advances in the status and job performance literatures, we extend theory by testing a structural model proposing interpersonal effectiveness constructs as the main explanatory mechanisms for self-monitoring’s status attainments. Our model fits the data well and shows that influence tactics of rationality and ingratiation, interpersonal performance, and interpersonal citizenship, partially mediate the effect of self-monitoring for status achievements. Finally, we rule out several alternative explanations for our findings–excluding other performance constructs, status antecedents, and different self-monitoring scales. Altogether, results meaningfully confirm, extend, and prune theory.
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 | 0.050 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.029 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".