Understanding the effects of various types of work modalities on employee change experiences
Bibliographic record
Abstract
Purpose Using the resource-drain perspective, this study has examined and compared the effects of work modalities, i.e., work from office, work from home, and hybrid work modalities, on various change experiences, including employees' work and family conflict, work engagement, job satisfaction, and job stress. Design/methodology/approach Empirical data were collected from 179 managerial-level employees from companies practicing different work settings to understand their change experiences while working in various work settings. T-tests, ANOVA, and ANCOVA were used to statistically analyze the proposed relationships. Findings Results suggested a relatively positive role of work-from-office modality on work and family outcomes in comparison to hybrid and work-from-home settings. In addition, work-from-office mode was found to have a positive impact on employees' work engagement and job satisfaction. Furthermore, the work-from-home modality was more likely to induce job stress among employees. Originality/value Recent literature has examined the role of either work-from-home or hybrid work settings on post-COVID-19 employees' work outcomes. However, the current study uses an in-depth psychological perspective, that is, resource drain theory, to offer a holistic comparison of the relative effects of various modalities on work-related and non-work change experiences of employees, such as work and family conflict, work engagement, job stress, and job satisfaction.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".