Remote “Helicopter Bosses”: Employee Perceptions of the Effects of Supervisory Controls and Remote Work During the <scp>COVID</scp>‐19 Pandemic*
Bibliographic record
Abstract
ABSTRACT The COVID‐19 pandemic precipitated an extensive involuntary shift to remote work and has since dramatically reshaped work and its supervision. We examine how employees' perceived productivity is associated with remote work during the pandemic, how supervisory controls moderate this relationship, and how remote work and perceived productivity impact employee preference for post‐pandemic remote work. We survey 589 workers in June 2020 via Mechanical Turk and find a directionally positive but nonsignificant association between remote work and perceived productivity. Additional analysis, however, indicates an indirect positive effect of remote work on perceived productivity through more hours worked. We further find supervisory monitoring intensity (SMI) is positively associated with perceived productivity, and the association between remote work and perceived productivity is more negative with more intense supervisory monitoring. Outcome‐based evaluation is also positively associated with perceived productivity, but it does not moderate the relationship between remote work and perceived productivity. Supervisor‐based evaluation has no significant association with perceived productivity, nor does it moderate the relationship between remote work and perceived productivity. Overall, our results suggest the main effect of remote work on perceived productivity during the pandemic is weak at best, and SMI is less compatible with remote work supervision than outcome‐based evaluation. Finally, we find increased remote work and perceived productivity improvement during the pandemic are positively associated with preference for post‐pandemic remote work, and we find a marginal positive interaction effect between them. These findings provide empirical evidence of the pandemic's repercussions on the post‐pandemic work environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".