Working in the digital economy: A systematic review of the impact of work from home arrangements on personal and organizational performance and productivity
Bibliographic record
Abstract
Work-from-home has become an increasingly adopted practice globally. Given the emergence of the COVID-19 pandemic, such arrangements have risen substantially in a short timeframe. Work-from-home has been associated with several physical and mental health outcomes. This relationship has been supported by previous research; however, these health and safety issues often receive little resources and attention from business perspectives compared to organizational and worker performance and productivity. Therefore, aligning work-from-home practices with business goals may help catalyze awareness from decision makers and serve to effectively implement work-from-home policies. We conducted a review to synthesize current knowledge on the impact of work-from-home arrangements on personal and organizational performance and productivity. Four large databases including Scopus, PubMed, PsychInfo, and Business Source Complete were systematically searched. Through a two-step screening process, we selected and extracted data from 37 relevant articles. Key search terms surrounded two core concepts: work-from-home and productivity/performance. Of the articles published prior to the COVID-19 pandemic, 79% (n = 19) demonstrated that work-from-home increased productivity and performance whereas 21% (n = 5) showed mixed or no effects. Of the articles published during the pandemic, 23% (n = 3) showed positive effects, 38% (n = 5) revealed mixed results, and 38% (n = 5) showed negative effects. Findings suggest that non-mandatory work-from-home arrangements can have positive impacts on productivity and performance. When work-from-home becomes mandatory and full-time, or external factors (i.e., COVID-19 pandemic) are at play, the overall impacts are less positive and can be detrimental to productivity and performance. Results will help foster an understanding of the impact of work-from-home on productivity and performance and inform the development of organizational strategies to create an effective, resilient, and inclusive work-from-home workplace by helping to effectively implement work-from-home policies that are aligned with business goals.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".