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The Future of Workplace Design: Pred...

Srivastava, Charu.

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The Future of Workplace Design: Predicting Perceived Work Performance and Well-Being.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Future of Workplace Design: Predicting Perceived Work Performance and Well-Being./
Author:	Srivastava, Charu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	199 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: A.
Contained By:	Dissertations Abstracts International85-03A.
Subject:	Architectural engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30521514
ISBN:	9798380412469

The Future of Workplace Design: Predicting Perceived Work Performance and Well-Being.
Srivastava, Charu.

The Future of Workplace Design: Predicting Perceived Work Performance and Well-Being. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 199 p.

Source: Dissertations Abstracts International, Volume: 85-03, Section: A.

Thesis (D.Des.)--Harvard University, 2023.

This item must not be sold to any third party vendors.

During the pandemic, millions of workers transitioned from working in office buildings to working at home, bringing into focus worker perceptions of their physical work environment. Currently, limited data is available on the role of the physical work environment on biopsychosocial aspects of worker well-being in home and office (hybrid) workplaces. Additionally, utilization of complex statistical tools for quantifying the impact of the interaction of multiple workplace spatial attributes using a holistic, systems-based approach has also not been fully explored. Machine learning tools hold great potential for understanding occupant behavior and predicting human perceptions in future scenarios. This research analyzed workplace design using a data-driven approach to measure and model the impact of spatial attributes of home and office workplaces on perceived productivity, physical activity, comfort, and sense of connection.In the first study, a nationwide survey (N=617) administered during the pandemic indicated that overall perceived work performance was higher at the office, while perceived well-being and comfort were higher when working at home. Machine learning models found that temperature, noise, and furniture were the best predictors of work performance, while access{A0}to amenities and the outdoors predicted physical activity and social interaction. To complement the birds-eye snapshot of the large-scale survey, a small-scale study (N=15) involving fine-grained wearable data and qualitative assessments examined participants working in a hybrid situation. Real time data from wearable devices revealed that participants had significantly greater step counts on days when they worked in an office. Machine learning models also showed that stair use, time spent walking while commuting to office or during breaks at home were significant predictors of increased daily step count. Lastly, the third study developed a framework to computationally create architectural images of workplaces and crowdsource perceptions (N=23,820) of productivity, comfort, and connection to coworkers. The machine learning models trained in this study predicted perceptions of new, "unseen" office workplace images based on spatial attributes present in the image.Insights from the studies can inform the design of future workplaces that optimize the benefits of working at home and in an office to promote health and well-being. Moreover, the novel user-perception-informed methodology developed through this research can serve as a pioneering framework for designers to better understand and predict the impact of their yet-unbuilt environments.

ISBN: 9798380412469Subjects--Topical Terms:

3174102
Architectural engineering.
Subjects--Index Terms:

Workplace design

The Future of Workplace Design: Predicting Perceived Work Performance and Well-Being.
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500 $a Advisor: Samuelson, Holly.
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520 $a During the pandemic, millions of workers transitioned from working in office buildings to working at home, bringing into focus worker perceptions of their physical work environment. Currently, limited data is available on the role of the physical work environment on biopsychosocial aspects of worker well-being in home and office (hybrid) workplaces. Additionally, utilization of complex statistical tools for quantifying the impact of the interaction of multiple workplace spatial attributes using a holistic, systems-based approach has also not been fully explored. Machine learning tools hold great potential for understanding occupant behavior and predicting human perceptions in future scenarios. This research analyzed workplace design using a data-driven approach to measure and model the impact of spatial attributes of home and office workplaces on perceived productivity, physical activity, comfort, and sense of connection.In the first study, a nationwide survey (N=617) administered during the pandemic indicated that overall perceived work performance was higher at the office, while perceived well-being and comfort were higher when working at home. Machine learning models found that temperature, noise, and furniture were the best predictors of work performance, while access{A0}to amenities and the outdoors predicted physical activity and social interaction. To complement the birds-eye snapshot of the large-scale survey, a small-scale study (N=15) involving fine-grained wearable data and qualitative assessments examined participants working in a hybrid situation. Real time data from wearable devices revealed that participants had significantly greater step counts on days when they worked in an office. Machine learning models also showed that stair use, time spent walking while commuting to office or during breaks at home were significant predictors of increased daily step count. Lastly, the third study developed a framework to computationally create architectural images of workplaces and crowdsource perceptions (N=23,820) of productivity, comfort, and connection to coworkers. The machine learning models trained in this study predicted perceptions of new, "unseen" office workplace images based on spatial attributes present in the image.Insights from the studies can inform the design of future workplaces that optimize the benefits of working at home and in an office to promote health and well-being. Moreover, the novel user-perception-informed methodology developed through this research can serve as a pioneering framework for designers to better understand and predict the impact of their yet-unbuilt environments.
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