What goes around comes around - work characteristics as both antecedents and outcomes of hybrid work adoption
Bibliographic record
Abstract
The effectiveness of hybrid work (a combination of telework and office-based work) has been theorized to operate through improvements in employees’ control over their workflow and work environment. However, current knowledge is largely based on static research designs and does not adequately consider the circumstances that drive employees’ actual adoption of hybrid work. In this study, we propose that adopting hybrid work improves four work characteristics tied to employees’ control over their workflow and work environment, namely autonomy, demanding environmental conditions (e.g. noise), workflow interruptions, and time pressure. At the same time, we also propose that employees’ actual hybrid work adoption depends on these same four work characteristics to begin with. Using a quasi-experimental study, we employ latent change score modelling to analyse how autonomy, demanding environmental conditions, workflow interruptions and time pressure influence, and are influenced by, hybrid work adoption, based on a sample of 699 white-collar workers. Results show that whereas autonomy and workflow interruptions drive hybrid work adoption, demanding environmental conditions and workflow interruptions change as a result of it. This suggests that organizational hybrid work policies can largely improve job demands, but that only a select group of employees adopt hybrid work.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".