Normative Legitimacy Management and the Expansion of Purpose-Driven Workforces through Organizational Identity
Bibliographic record
Abstract
Social-political legitimacy requires leaders to do things right (normative legitimacy) and correctly (regulatory legitimacy). However, it is more challenging to manage normative legitimacy in diverse organizations. Leaders use normative legitimacy to help align organizational values to the social environment in which it operates. The ability to manage normative behaviors is an ethical virtue and may establish a link with organizational identity. This research applies the leadership ethics and decision-making (LEAD) model. The LEAD model suggests that employee perception of ethics requires leaders to conduct an outward examination of their decisions using integrity, assurance, and pragmatism. Previous research suggests that the LEAD model may act as an ethical guide to "doing things right" and potentially fill the gap in managing normative legitimacy by influencing organizational identity. The results conclude that outward examinations account for employee perceptions and that the LEAD model is a suitable ethical leadership concept. Integrity, assurance, and pragmatism have significant positive relationships with and predict organizational identity. The findings reveal that the LEAD model discerns ethical leadership behavior, appropriately manages normative legitimacy, and creates a purpose-driven workforce by developing organizational identity.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".