Work-From-Home Engagement during COVID-19: Implications for Human Resource Management
Bibliographic record
Abstract
Work-from-home has gained swift and massive adoption due to technological advancement and the large-scale disruption evoked by the COVID-19 pandemic. Work-from-home, also called telecommuting, involves performing work/business responsibilities from a non-office location or typically from home. The work-from-home format has garnered acceptance as the alternative to traditional office work, but it is not without disadvantages. Thus, a thorough insight into the mechanism of the work-from-home format and how it relates to engagement is necessary for organizational leaders and human resource practitioners to cultivate engagement of remote workers. This article illustrates the current state of scholarly research on work-from-home engagement by using the lens of an integrated literature review. This article explains the forces accompanying the work-from-home format and their interactions with employee engagement. The article proposes a conceptual framework of the work-from-home engagement field. The constructs of the work-from-home engagement field, which are the work-from-home positive forces, negative forces, and positive-negative forces, are explained, and the critical implications for human resource management are highlighted.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".