IMPACT OF WORKING FROM HOME ON PRODUCTIVITY & PERFORMANCE, EVIDENCE FROM NORTH AMERICAN LOGISTICS INDUSTRY DURING COVID-19 PANDEMIC
Bibliographic record
Abstract
This paper analyzes the impact of shifting all employees from office-based working to working from home (WFH) on productivity and performance. Before the COVID-19 Pandemic, there were no instances of companies moving all of their employees to WFH indefinitely. This is one of the first case studies analyzing this new phenomenon supported by hard criteria for productivity and performance measures. We define productivity as the quantity and performance as the quality of the output. We collected our data from a North American 3rd Party Logistics company and employed a multimethod approach to examine the effects of this organizational change forced by the COVID-19 Pandemic. We gathered data from the employees through video interviews and online surveys. We compiled the performance metrics from the corporate database and received commentary from top management. We also used national statistics databases to delineate the specific market conditions. Figures indicate evident improvements in revenues, profits, and labor productivity after the shift to WFH. We also examined other potential moderating factors, such as changes in market conditions, improvements in business processes, and/or management approaches, to distinguish the effect of remote working. Overall results show that when accompanied by solid leadership strategies and proper communications, and with investment in IT technologies, working from home improves productivity with little or no effect on performance. Our findings offer valuable insights for managers who need to make strategic decisions about WFH arrangements for their companies, especially brokerage-type businesses, more specifically those in the logistics industry. The study is based on data from a case study involving one company, preventing the authors from generalizing their findings.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".