Delivering bad news fairly: Higher construal level promotes interactional justice enactment through perspective taking
Bibliographic record
Abstract
Summary How can managers deliver bad news with greater interactional justice? We propose a novel cognitive pathway: Construing the activity at a higher (vs. lower) level increases actors' other‐oriented perspective taking, which in turn promotes the enactment of interactional justice. Three studies provide support. Studies 1 and 2 demonstrated a beneficial effect of construal level on interactional justice enactment when explaining a hypothetical bad news decision. Study 2 also showed that other‐oriented perspective taking is the mechanism through which construal level promotes interactional justice enactment. Study 3 replicated and extended these findings with a different paradigm and the addition of a moderator variable (trait perspective taking), providing a converging test of the proposed mechanism. Overall, the present research suggests that how managers think about delivering bad news—whether at higher or lower levels of construal—affects the extent to which they think from the recipient's perspective, and in turn how they communicate the news. Our research generates novel avenues for future research on justice enactment, construal level theory, and perspective taking. It may also have implications for better understanding downstream consequences of interactional justice enactment for bad news deliverers themselves.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".