IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i13p8217-d856365.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/13/8217/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/13/8217/
    Download Restriction: no
    ---><---

    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Farrukh Saleem & Abdullah Saad AL-Malaise AL-Ghamdi & Madini O. Alassafi & Saad Abdulla AlGhamdi, 2022. "Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review," IJERPH, MDPI, vol. 19(9), pages 1-18, April.
    2. Rasheed, Jawad & Jamil, Akhtar & Hameed, Alaa Ali & Aftab, Usman & Aftab, Javaria & Shah, Syed Attique & Draheim, Dirk, 2020. "A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    3. Jacques Bughin & Michele Cincera & Dorota Reykowska & Rafal Ohme, 2021. "Big Data is Decision Science: the Case of Covid-19 Vaccination," Working Papers TIMES² 2021-047, ULB -- Universite Libre de Bruxelles.
    4. Ballı, Serkan, 2021. "Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    5. Kalantari, Mahdi, 2021. "Forecasting COVID-19 pandemic using optimal singular spectrum analysis," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    6. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8217-:d:856365. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.