Improving service delivery: Investigating the role of information sharing, job characteristics, and employee satisfaction
Bibliographic record
Abstract
Purpose The purpose of this study is to propose and test a model designed to investigate the impact of job characteristics, employee satisfaction, and information sharing on two key indicators of quality service delivery, such as worker perceptions of their efficiency and customer focus. Design/methodology/approach During the project, 9,060 employees of a large national telecommunications organization in North America provided information in two surveys six months apart. The model was tested by using the PLS (Partial Least Squares) procedure. Findings The results found support for the proposed model, indicating that autonomy and challenging work contribute to employee satisfaction, and that employee satisfaction and information sharing relate to greater reported efficiency and customer focus. The results and their implications are discussed. Research limitations/implications The key limitation of this project is that the suggested model was tested in only one organization in one industry. In future, the nomological validity of the model should be confirmed in other settings. Practical implications The findings suggest that HR departments should cooperate with IT departments to promote high‐quality service delivery. Originality/value Whereas HR is traditionally the domain of employee satisfaction initiatives, it is the IT department that typically spearheads knowledge management initiatives. By coordinating these two ventures together, and aligning their goals, senior managers will be able to realize more favorable 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".