Interpersonal conflict and psychological well-being at work: the beneficial effects of teleworking and emotional intelligence
Bibliographic record
Abstract
Purpose This cross-sectional study had several objectives. This paper aims to study the direct effect of teleworking on interpersonal conflict, the mediating role that interpersonal conflict can play between teleworking and psychological well-being, the moderating role emotional intelligence (EI) can play between teleworking and interpersonal conflict and whether this moderation effect can, in turn, be associated with psychological well-being (moderated mediation effect). Design/methodology/approach Path analyses using Mplus software were performed on a sample of 264 employees from 19 small- and medium-sized organizations. Findings While teleworking was associated with lower interpersonal conflict, it was not associated with enhanced psychological well-being. Interestingly, workload seemed to be associated with higher interpersonal conflict, while decision authority and support garnered from one’s supervisor seemed to be associated with lower interpersonal conflict. Teleworking was indirectly associated with higher psychological well-being via interpersonal conflict. Finally, EI played a moderating role between teleworking and lower interpersonal conflict. This was, in turn, associated with higher psychological well-being. Practical implications EI is an essential skill to develop in the workplace. Originality/value A deepened understanding of the role played by EI at work could help organizations to provide positive work environments, both in person and online. This is especially relevant today, with the continued increase in teleworking practices and the resulting rapidly changing interpersonal relationships.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".