THE IMPACT OF WORKING FROM HOME ON PRODUCTIVITY. A STUDY ON THE PANDEMIC PERIOD
Notice bibliographique
Résumé
The study on working from home productivity has received much interest from researchers in the recent years. Numerous studies have found working from home to be productive. However, now with the coronavirus pandemic turning many more people into remote workers overnight, is working from home still productive? This is what the current study aims to find, taking into account that the employees have no choice during this pandemic period; they are forced to work from home. Before the pandemic, the employees were consent with working from home and usually they were coming in the working environment at least one day a week, but usually two or three days. Full-time working at home may be different. Moreover, the employees are now forced to work from home without any training or preparation. Most of them have never worked from home before. Even if the employees worked from home occasionally and they are trained for it, yet they are not prepared for such a long period away from their working environment. Working from home still has its advantages: there are no face-to-face meetings, no distractions from co-workers, no annoying managers to boss them around, no wasted time in traffic, no worries about the children’s safety, as they are at home. Besides the advantages, working from home comes also with some disadvantages. The specialists worry about the negative effects, such as an explosion of mental health issues that could also generate physical health problems. The current study focuses on finding the advantages and disadvantages of working from home, but also on ways to make working from home more effectively. For this purpose, a questionnaire was administered to the employees from three private companies in Bihor county. The final results indicate a negative effect of working from home on productivity. We have found the main benefits and challenges and also the ways to improve productivity when working from home.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».