It’s the act that counts: minimizing post-violation erosion of trust
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the effects of damage incurred by the trustor as a result of a trust violation and the impact of different levels of post-violation trust repair behaviours by the trustee on the subsequent erosion of trust. Design/methodology/approach – Data were collected from 232 middle to senior level managers using a two-part scenario-based experimental design to test the impact of damage incurred (avoided) and post-violation repair behaviour. Respondents’ levels of trust were measured pre- and post-violation as well as forgiving and a range of demographic variables. Findings – Results showed that trust eroded independent of the level of damage that may have been caused. Further, post-violation trust repair behaviour by the trustee led to a significantly lower erosion of trust as compared to not engaging in such behaviours. Furthermore, erosion of trust was minimized, when the trustee engaged in increasing levels of trust repair behaviour. Results also showed that trustors who were relatively more forgiving were less likely to lose trust in the trustee after a violation. Research limitations/implications – In this study we focused on two key factors influencing the erosion of trust. Further factors need to be identified and empirically tested in order to get a more holistic view on how trust erodes. The results serve as one step towards building an integrated model of trust erosion. Practical implications – For practicing managers, the results imply that the actual incurrence or avoidance of damages from a trust violation appears to be peripheral – trustors are more concerned about the violation as a principle and a harbinger of similar future incidents. Further, quickly engaging in trust repair behaviours, such as offering an a good explanation, a heartfelt apology, and appropriate remedy, helps minimize the erosion of trust. Originality/value – This paper addresses an under-investigated facet of trust research in organizations – erosion of trust – which is especially crucial in light of the growing awareness that most organizational relationships actually start off with high levels of trust rather than low trust. Thus, this study offers insights into maintaining (as opposed to building) trust.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".