Effect of Elaborateness of Apology on Subsequent Disciplinary Action Considering Outcome Severity and Favorable Reputation as Moderators
Bibliographic record
Abstract
Objectives: Both managers and scholars have strong reason to understand the human response of apology in different degrees and under different circumstances as a possible influencer of punishment of employees for violating workplace rules. The purpose of this study is to investigate the effect of apology on subsequent disciplinary action, considering different levels of elaborateness of apology, severity of outcomes, and favorableness of reputation. Methods: A 3 × 2 × 2 between subject factorial design experiment was conducted with 262 participants. The dependent variable was discipline, and three independent variables included the elaborateness of apology, the offender’s reputation, and the severity of outcome resulting from the violation. Collected data was analyzed using ANOVA and planned comparisons. Results: The claim that apology leads to less punishment was partially supported. Although there was no statistically significant support for an apology’s effect on a 6-item composite measure of disciplinary action, the effects of apology on individual items such as dismissal showed significance. There was also support for the effects of severity of outcome and reputation of the offender on the level of disciplinary action recommended. Conclusions: The results of the present study demonstrate that the issue of apology’s effect on discipline is more complex than once thought. Thus greater consideration should be taken in efforts to achieve a better understanding of its effects.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".