Understanding Employee Preferences towards Flexible Work Arrangements for the COVID-19 Post-Pandemic Period
Bibliographic record
Abstract
This research examines employees’ preferences toward post-pandemic flexible work arrangements (FWA) using logit modelling techniques. First, it conducts an online questionnaire survey among working professionals in the Halifax Regional Municipality (HRM), Canada, to gather information on their socio-demographics, travel characteristics and work arrangements before and during the pandemic, and their preferences towards post-pandemic work from home (WFH) and flexible work hours (FWH). Using the collected 337 complete responses, then it develops a binary logit model (BLM) to examine factors affecting employees’ WFH choice, a multinomial logit model (MLM) to examine the choice of WFH frequency, and an ordered logit model (OLM) to investigate preference for FWH. Results show that approximately 55% of the employees prefer WFH, while almost half of the respondents prefer WFH a few days/week. Logit model results indicate that increasing WFH preference also increases FWH preference, while employees who perceive higher importance of FWH tend to choose higher WFH frequency. Employees with higher commute frequency during the pandemic are less likely to prefer FWA post-pandemic. Increasing commute time increases the willingness to have FWH. The results will assist transport policymakers in better understanding the factors influencing employees’ FWA choices which will aid in developing effective TDM policies.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".