Employment expectations: examining the effect of psychological contract fulfillment on bridge employees' personal and work attitudes
Bibliographic record
Abstract
Purpose In this study the authors examine whether bridge employees tend to hold non-standard jobs, and if so, whether non-standard job choice is deliberate. Moreover, the authors examine whether fulfillment of employment expectations affects the personal and work attitudes of bridge employees. Design/methodology/approach The authors' research and hypotheses are supported and developed through psychological contract theory. The authors collected data from 195 bridge employees, employed in a variety of jobs, through an online survey. Hypotheses were tested using hierarchical multiple regression. Findings This study suggests that some bridge employees may engage in non-standard employment deliberately. Moreover, we show that fulfillment of perceived obligation by employers (psychological contract) is associated with personal and work attitudes (life satisfaction, job satisfaction, affective commitment, normative commitment and intentions to stay) of bridge employees. Research limitations/implications While this study supports psychological contract theory as an important framework for understanding bridge employment, sample size, cross-sectional data and a lack of diversity in the sample limit causality, generalizability and data robustness. Future research should strive to replicate and extend the current findings. Practical implications The present study underlines the importance of designing jobs to meet the expectations of bridge employees. Also, it highlights the preference of bridge employees to engage in non-standard employment. Originality/value The authors extend bridge employment research by empirically examining the relationship between unmet employment expectations and the personal and work attitudes of bridge employees.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".