High Tech Tethers and Work-family Conflict: A Conservation of Resources Approach
Bibliographic record
Abstract
This is one of the first studies to empirically examine the relationship between wireless communications technology and work interference with family, and results shed light on the motivating factors that influence individuals to continuously engage with mobile technologies, sometimes to their personal detriment. We draw from Conservation of Resources (COR) theory to examine the relationship between using mobile communications technologies during non-work time (e.g., evenings, weekends, vacation) and psychological variables related to work-family conflict and well being, and whether this relationship is mediated by perceptions of job control and detachment from work. We collected data from 139 full-time working adults from a large media organization and analyzed it by conducting two multiple mediation regression models using bootstrapping procedures (Preacher & Hayes, 2008). Results revealed that higher levels of mobile technology use during evenings, weekends and vacations were directly related to higher levels of work-family conflict, operationalized as work interference with family. Technology use was also related to both resource enhancing and resource depleting variables. Specifically, technology use was positively related to job control and negatively related to detachment from work. Job control and detachment from work, in turn, were negatively related to work interference with family. Findings suggested that the mediating effect of detachment on the relationship between technology use and work interference with family was greater than the mediating effect of job control, thus providing evidence to support the COR theory principle that resource loss is more salient than resource gain.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".