The role of academics’ socio-demographic characteristics as moderating in WFH productivity: Empirical evidence
Bibliographic record
Abstract
The present study examines the faculty staff’s socio-demographic factors (i.e., gender, academic ranking, and experience) as moderating variables on the relationship between the four interrelated factors (organizational, individual, technological, and client engagement) and their productivity (performance) during the Covid‐19 pandemic. A conceptual framework was developed by integrating several relevant studies in the field of Work from Home (WFH) productivity. To end this, we involved (n=388) academic staff working from home during the Covid-19 crisis to test the hypotheses in the higher education context. The findings showed that the academics’ WFH productivity was significantly associated with the four interrelated factors (organizational, individual, technological, and client-related factors) either collectively or individually, and the most important one was the individual-related factors. The moderation analysis reveals that the effect of the socio-demographic characteristics (gender, academic ranking, and experience) on WFH productivity was varied. Surprisingly, the study findings provided evidence for the first time that the client’s engagement (student) factor, which has not been studied before, was found as one of the main determinant factors of WFH productivity during the Covid-19 crisis.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".