Breaking Promises, Breaking Trust: How Psychological Contract Breach Shapes Trust Trajectories
Bibliographic record
Abstract
Trust is a cornerstone of effective organizational functioning through employee performance. This study investigates the dynamic nature of trust, focusing on how psychological contract breach—a perception that an organization has failed to meet its obligations—predicts divergent trust trajectories and their implications for task accuracy. Drawing on social exchange theory and attribution theory, we argue that breaches negatively influence trust trajectories by lowering baseline trust and hindering its positive development. Using latent class growth modeling across four bi-annual measurement points among 1,135 Belgian employees, we identified two distinct trust trajectories: trust accumulation (13.13%) and trust erosion (86.87%). Psychological contract breach significantly predicted the growth parameters of these trajectories, accelerating trust erosion and dampening trust accumulation. Moreover, these trust trajectories were linked to performance: employees in the trust accumulation trajectory exhibited significantly higher task accuracy rates compared to those in the trust erosion trajectory. The findings contribute to psychological contract and trust literatures by emphasizing the temporal dynamics of trust and the heterogeneous impacts of breaches. Practically, the study underscores the importance of trust-focused interventions to mitigate breach effects and enhance performance outcomes. Proactive trust management and tailored organizational strategies are vital for fostering resilient, high-performing teams, even in challenging environments.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".