Trajectories of Teleworking via Work Organization Conditions: Unraveling the Effect on Work Engagement and Intention to Quit with Path Analyses
Bibliographic record
Abstract
Several countries are currently experiencing worker shortages. In this context, which favors employees, employers must improve their offer to attract and retain employees, not only in regards to wage but also in regards to work organization conditions. Teleworking is one work organization condition (or human resource management practice) that is receiving increasing attention due to its increased prevalence in recent years. This cross-sectional study’s objective was to verify the influence of teleworking on work engagement and the intention to quit through its effects on work organization conditions (e.g., social support, workload, recognition, skill utilization, and number of hours worked). This study was based on the demands-resources model as teleworking can represent a demand or a resource and is likely to influence work organization conditions. Path analyses were carried out using Mplus software. A sample of 254 French Canadian staff members (n = 254) from 19 organizations (small and medium-sized). The results indicate that teleworking is indirectly associated with a higher level of work engagement through its effect on skill utilization. Moreover, teleworking is indirectly and negatively associated with the intention to quit through its impact on skill utilization and work engagement. More specifically, teleworking is associated with an overall lower intention to quit. This study aimed to shed light on the mechanisms underlying the associations between teleworking, work engagement, and the intention to quit. Considering work organization conditions in this sequence modifies the effect of teleworking on both outcomes. Although it can be harmful (i.e., negatively associated with work engagement) when the work organization conditions are not considered, its positive influence on skill utilization reverses this effect. From a practical perspective, it seems crucial to ensure that teleworkers can use their skills to promote the success of its implementation.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".