Why don't I trust you now? An attributional approach to erosion of trust
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the effects of the trustor's responsibility‐attributions for a trust violation and the trustee's frequency of prior violations on the subsequent erosion of trust in the relationship. Design/methodology/approach Data were collected from 120 middle‐senior level managers using a two‐part scenario‐based experimental design to test the impact of attributions and frequency of violations. Respondents' levels of trust and distrust were measured pre‐ and post‐violation as well as forgiving and a range of demographic variables. Findings Results showed that trust eroded (and distrust increased) more when trustors perceived the trustees as not wanting to fulfill the trust‐expectations than when they could not do so. Further, trustors were willing to tolerate a maximum of two violations before trust in the relationship eroded significantly. The results also showed that trustors who were relatively more forgiving were less likely to lose trust in the trustee after a violation, as were younger and less experienced individuals. Research limitations/implications Although scenario‐based experiments assess the cognitive states of the respondents rather than actual behaviors, they serve as a valuable first step. By highlighting the two‐step sequence that may underlie the trust erosion process and emphasizing the importance of using an attributional perspective, the paper invites future research on a range of factors such as patterns of violation, degrees of damage, etc. Collectively, they ought to lead to an integrated model of trust erosion. Practical implications For practicing managers, the results underscore the importance of maintaining trust by constantly meeting expectations. While they may be forgiven for one‐time mistakes in maintaining trust, they cannot be repeated without severely damaging the trust in the relationship. Also, employees need to be convinced that the erring manager or colleague has done his/her very best to prevent the violation. 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 | 0.031 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".