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Transforming Residential Interiors into Workspaces during the COVID-19 Pandemic

Author

Listed:
  • Begüm Ulusoy

    (Interior Architecture and Design, School of Architecture and the Built Environment, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UK)

  • Rengin Aslanoğlu

    (Institute of Spatial Management, Wrocław University of Environmental and Life Sciences, Grunwaldzka 55, 50-357 Wrocław, Poland)

Abstract

Residential interiors (RIs) have been designed by anonymous designers throughout history and have reflected their users’ identity, culture, and habits until modern times, although design and architecture courses rarely involve residential interiors in their curriculums. Therefore, decision-makers (architects, interior architects, designers, and users) took them for granted. However, COVID-19 forced revisiting this approach towards RIs and they faced a gap in the literature helping them to design these interiors, especially workspaces, in order to improve their users’ experience. In connection with previous studies, which explored creativity in workspaces, this study aims to compile colour-related literature work on workspaces in RIs (WRI) which will require further attention from interior architects to reconsider the discipline under new normal conditions. Providing a framework for WRIs in terms of function and activity might lead to the semantics of RIs in future studies. This study’s findings contribute to the interpretation and understanding of new normal workspace interiors after the COVID-19 pandemic so it will be beneficial for decision-makers in addition to researchers who aim to investigate this topic in future studies.

Suggested Citation

  • Begüm Ulusoy & Rengin Aslanoğlu, 2022. "Transforming Residential Interiors into Workspaces during the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(13), pages 1-13, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8217-:d:856365
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    References listed on IDEAS

    as
    1. Yadav, Milind & Perumal, Murukessan & Srinivas, M, 2020. "Analysis on novel coronavirus (COVID-19) using machine learning methods," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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