Peer effects on organizational commitment: Evidence from military cadets
Bibliographic record
Abstract
The commitment of soldiers to the military is essential because it could lead to increased morale, motivation and retention. Despite the accumulation of knowledge about predictors of organizational commitment (OC), efforts to investigate environmental factors influencing OC are in their infancy. We note that individuals shape their attitudes toward the environment based on information obtained from their surroundings, and we investigate peer effects on OC using data from a natural experiment of randomly-assigned military academy roommates. A total of 400 cadets (Sex ratio: 93.5% male, Age: 21.13 ± 1.43 years) from 136 living quarters participated in this quantitative study. In both self- and roommate-reports, we found that the average affective commitment (AC), continuance commitment (CC), and normative commitment (NC) of roommates in a living quarter can still predict AC, CC, and NC of the remaining individual in that same living quarter, respectively, even after controlling for the personal predictors of that remaining individual. Additionally, in self-report, we discovered that when there is a high heterogeneity in AC among roommates within a living quarter, the AC of the remaining individual in that living quarter tends to be higher, even after controlling for the personal predictors of that remaining individual. These findings provide initial evidence that attempting to assign soldiers with low OC to the same living quarters as those with high OC may be worthwhile.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".