What About My Occupation? A Multidimensional View of Workplace Identification and Unethical Pro-Organizational Behavior
Bibliographic record
Abstract
Abstract Focusing on the individual unit of analysis, we explore how workplace identification can explain why individuals engage in unethical behavior that benefits an organization (unethical pro-organizational behavior; UPB). Social identity theory (SIT) stipulates that we want membership within high status organizations and, at extreme levels, may put the organization’s needs above all else. In taking a holistic approach to identification, we investigated how a strong occupational identification can mitigate this desire to unethically help an organization; occupations are a separate identity source and contain codes of conduct that guide ethical behavior. Utilizing a sample of 236 accountants and financial professionals, results indicated that organizational identification and occupational identification alone did not significantly predict UPB, however, the interaction of these identities did. More specifically, organizational identification significantly positively predicted UPB only when occupational identification was extremely low in strength. This effect was found after controlling for relevant personality and cognitive mechanisms related to unethical behavior. Implications for a multidimensional identification view of unethical behavior are discussed.
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 | 0.001 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".