The job resources-engagement relationship: the role of location
Bibliographic record
Abstract
Purpose This study investigates the moderating role of employee office location in the relationship between support-related job resources (i.e. organizational support for development, supervisor support) and work engagement among public sector employees. Design/methodology/approach An online questionnaire was completed by 2,206 digital services branch of public service employees in Canada. Structural equation modeling (SEM) was used to test office location as a moderator of job resources and work engagement. Findings The results indicate that office location moderates the relationship between organizational support for development and work engagement, such that this relationship is stronger for head office employees. Conversely, results show office location moderates the relationship between supervisor support and work engagement, such that this relationship is stronger for regional office employees. Research limitations/implications The questionnaire was self-report in nature and from a single department. Future research should consider multiple sources of reporting and additional departments. Practical implications The current study suggests that to increase work engagement, public sector organizations need to offer head office employees more organizational support for development and regional employees more supervisor support. Originality/value The literature on public sector work engagement tends to study job resources as having universal effects on work engagement regardless of employees' place of work. This study suggests that certain resources matter more depending on office location.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".