The Relationship Between Psychological Contract Breach and Work Motivation and Attitude Among Tech Employees
Bibliographic record
Abstract
There has been some research on moderating factors of the relationship between psychological contract breach (PCB) and work engagement and affective commitment. However, most studies conducted have mainly focused on situational factors (e.g., job satisfaction, exchange imbalance), not on individual factors (e.g., personality traits). This research gap leaves a question in the role that personality traits play in influencing the negative consequences of PCB. The purpose of this study was to examine the moderating role of conscientiousness on the relationship between PCB and two outcomes, work engagement and affective commitment. Hypotheses were tested using 103 technology industry employees in the United States or Canada in an online survey. Although the results of this study did not support the hypothesis that conscientiousness would buffer the negative relationship between PCB and work engagement and affective commitment, they revealed that PCB indeed had a negative relationship with work engagement and affective commitment. Furthermore, results showed that conscientiousness was directly related to work engagement. Organizational actions to reduce the occurrence of PCB are discussed, including establishing clear and honest communication about expectations and obligations between the organization and its employees and careful assessment of employees’ needs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".