Attachment, stress and interactional justice
Bibliographic record
Abstract
Purpose This paper aims to examine how individuals' attachment dispositions relate to interactional justice perceptions, how work stressors moderate this association, and how together they associate with attitudes (satisfaction, turnover intention, commitment) and citizenship behaviors at work. Design/methodology/approach Survey data were used in an observed variable path analysis examining mediation by interactional justice and moderation by stressors on the associations between attachment dimensions and work outcomes. Findings Attachment avoidance was negatively related to interactional justice perceptions and attachment anxiety was also negatively related to interactional justice perceptions, but only under conditions of higher work stressors. Interactional justice mediated the associations between attachment avoidance and work outcomes, and between the interaction of attachment anxiety and work stressors on work outcomes. Practical implications These findings are particularly relevant to multiple aspects of HR practice, including performance feedback, managing stressors, building resilience, reward allocation and recognition, designing wellness programs and other aspects of human resource management. Originality/value This research goes beyond contextual predictors of justice perceptions and demonstrates that jointly considering attachment dimensions and work stressors uniquely contributes to understanding the formation of justice perceptions and their combined influence on work attitudes and 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".