Supervisors Don’t Do It Alone: How Role Models Affect Employee Ethical and Safety Behavior at Work
Bibliographic record
Abstract
Organizational research has drawn on social learning theory (Bandura, 1971) to explain how employees learn workplace behaviors, particularly focusing on the influence of supervisors while overlooking the influence of other role models within and outside the workplace. Moreover, a tendency to employ social learning without directly measuring the role modeling process has led to speculation about the explanatory power of social learning theory in some behavioral contexts (e.g., Wo, Ambrose, & Schminke, 2015) and questions about the extent to which supervisors are necessarily the most salient role model for employees (Ogunfowora, 2014). We address these issues by developing a measure of role modeling perceptions and using it to test a model of multiple role modeling effects on employee behavior at work in two distinct behavioral contexts: workplace ethics and safety. We find that the effects of specific role models on employee behavior depend on the behavioral context. Employee ethical conduct at work is influenced by role models from within and outside the organization, whereas employee safety behaviors are influenced only by workplace role models. In addition, our model integrates social learning and social exchange theories to explain how organizational role models uniquely motivate behavior by fostering feelings of obligation to the organization.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".