Working from Home in Italy during COVID-19 Lockdown: A Survey to Assess the Indoor Environmental Quality and Productivity
Auteur(s): |
Francesco Salamone
Benedetta Barozzi Alice Bellazzi Lorenzo Belussi Ludovico Danza Anna Devitofrancesco Matteo Ghellere Italo Meroni Fabio Scamoni Chiara Scrosati |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Buildings, 23 novembre 2021, n. 12, v. 11 |
Page(s): | 660 |
DOI: | 10.3390/buildings11120660 |
Abstrait: |
Italians were the first European citizens to experience the lockdown due to Sars-Cov-2 in March 2020. Most employees were forced to work from home. People suddenly had to share common living spaces with family members for longer periods of time and convert home spaces into workplaces. This inevitably had a subjective impact on the perception, satisfaction and preference of indoor environmental quality and work productivity. A web-based survey was designed and administered to Italian employees to determine how they perceived the indoor environmental quality of residential spaces when Working From Home (WFH) and to investigate the relationship between different aspects of users’ satisfaction. A total of 330 valid questionnaires were collected and analysed. The article reports the results of the analyses conducted using a descriptive approach and predictive models to quantify comfort in living spaces when WFH, focusing on respondents’ satisfaction. Most of them were satisfied with the indoor environmental conditions (89% as the sum of “very satisfied” and “satisfied” responses for thermal comfort, 74% for visual comfort, 68% for acoustic quality and 81% for indoor air quality), while the layout of the furniture negatively influenced the WFH experience: 45% of the participants expressed an unsatisfactory or neutral opinion. The results of the sentiment analysis confirmed this trend. Among the Indoor Environmental factors that affect productivity, visual comfort is the most relevant variable. As for the predictive approach using machine learning, the Support Vector Machine classifier performed best in predicting overall satisfaction. |
Copyright: | © 2021 by the authors; licensee MDPI, Basel, Switzerland. |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
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sur cette fiche - Reference-ID
10648350 - Publié(e) le:
10.01.2022 - Modifié(e) le:
01.06.2022