Work-from-home adjustment in the US and Europe: the role of psychological climate for face time and perceived availability expectations
Bibliographic record
Abstract
The COVID-19 pandemic has precipitated a massive adoption of high-intensity work-from-home (WFH), a form of work organization that is expected to persist. Yet, little is known about the predictors and mechanisms underlying employees’ successful adjustment to high-intensity WFH. Drawing on signaling theory, we identify psychological climate for face time (i.e., an employee’s perception that their organization values physical presence in the office) as an antecedent of WFH adjustment. We argue that when WFH employees perceive that their organization encourages face time, they may view availability as a signal of their dedication to work, replacing visibility. Consequently, they feel expected to be extensively available (e.g., check emails outside of regular working hours). In turn, these perceived expectations predict lower adjustment to WFH. We further explore whether this process differs in the US and two European countries, France and Spain, given different employment protection and right to disconnect legislations, and different meanings attached to work ethics. In a two-wave study on a sample of 532 full-time WFH employees, structural equation modeling analyses show that perceptions of availability expectations mediate the negative relationship between psychological climate for face time and WFH adjustment, and that this process is accentuated in the US.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".