Trust and the Design of Work Complementary Constructs in Satisfaction and Performance
Bibliographic record
Abstract
The article presents results that indicate that trust and job design are complementary concepts in understanding outcomes like intention to quit and satisfaction. We conceptualized a worker's beliefs that a supervisor can be trusted as being composed of three main elements - beliefs in the supervisor's predictability, benevolence and fairness. This was motivated in part by a desire to conceptualize trust in a way that distinguished it from leader-member exchange (LMX) quality. The capacity of this measure of trust to predict self-reported outcomes was then compared with a job's motivational potential score, as a way of testing the trust measure's criterion validity. To do so, the results from two separate surveys were analysed. The first was based on the questionnaire responses of 535 employees in the telephone industry in the province of British Columbia; the second, of 230 service station employees from across Canada. In the studies reported here, supervisor relationships accounted for a significant amount of the variance on a variety of criterion measures. The results also suggested that perceptions of trust act independently of job design factors in affecting the outcome variables of absence, intention to quit, satisfaction and performance. In addition, the results indicated trust to be as important as job design factors in predicting outcomes.
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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 | 0.020 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 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".