Bibliographic record
Abstract
Purpose The purpose of this study is to assess the pathway through which authentic leadership influences organizational citizenship behavior (OCB). The authors examine how the perception of overall fairness and a sense of belongingness mediate the relationship between authentic leadership and OCB. Design/methodology/approach The authors distributed survey questionnaires to full-time employees working for an insurance company. Data were collected in two phases. To test the hypotheses, the authors conducted hierarchical multiple regression analysis using the PROCESS macro by Hayes (2012). Findings PROCESS analysis reveals that overall fairness mediates the relationship between authentic leadership and subordinates' sense of belongingness, which is then positively related to OCB. Taken together, these findings are largely in line with the authors’ theoretical model. Originality/value Empirical research has yet to explore how authentic leaders create the perception of fairness, which influences subordinates' OCB. Thus, this study extends the authors’ knowledge on the extant literature of organizational behavior by integrating two important domains—authentic leadership and organizational fairness—to propose that authentic leadership is a fair leadership that aids in promoting OCB. Also, studies on authentic leadership processes have examined basic models and neglected the possibility of sequential mediation. To better understand the complex relationship of authentic leadership and OCB, the authors examine overall fairness and belongingness as sequential mediators.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".