Unpacking the link between organizational justice and innovative behavior: a meta-analytic review across sectors
Bibliographic record
Abstract
Studies have reported mixed findings on whether organizational justice effectively promotes innovative behavior. However, the existing literature often lacks a quantitative assessment of how these constructs interact. This meta-analysis seeks to bridge that gap by synthesizing findings from various studies that explore the effects of organizational justice and its dimensions—distributive, procedural, and interactional—on innovative behavior. This meta-analysis, conducted following the PRISMA protocol and based on 32 articles, reveals a consistent positive association between organizational justice and innovative behavior, with each dimension contributing to this relationship. Furthermore, the analysis identifies a moderating effect of sector type (private vs public), specifically affecting the link between procedural justice and innovative behavior. This finding enriches the discussion on sectoral differences and emphasizes the need for further investigation into how different organizational environments influence justice-driven innovation. Overall, this study contributes to the theoretical validation of social exchange theory and offers practical insights, encouraging a dialogue between the private and public sectors on leveraging organizational justice to foster innovative behavior.
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 | 0.040 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.017 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".