Interpersonal justice and creativity: testing the underlying cognitive mechanisms
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the underlying cognitive mechanisms between interpersonal justice and creativity. Design/methodology/approach The theoretical model was tested through survey method in two distinct settings, i.e. student teams and organizational setting. Findings This study found evidence that interpersonal justice has an indirect relationship with creative behavior through two distinct paths of psychological meaningfulness and psychological availability in Study 1 and through psychological availability in Study 2. The results clarify and support the proposition in the justice literature that interpersonal fairness is relevant to creativity because of its relationship to risks associated with creativity, and that this affect holds when controlling for procedural, distributive and informational justice (Study 2). Research limitations/implications The results suggest that interpersonally fair supervision has a significant influence on employees’ creativity. Fair supervisory treatment adds value to the organization and contributes to the well-being of employees by directly influencing perceptions of psychological engagement factors of meaningfulness and availability of resources. Originality/value This study contributes to the justice, creativity and psychological engagement literatures by exploring the mechanisms linking organizational justice and creativity in a non-Western context.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".