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Learnings from User Digital Trail Post-Occupancy Evaluation before COVID-19 for Future Workplace Analysis and Design

Author(s):




Medium: journal article
Language(s): English
Published in: Buildings, , n. 11, v. 11
Page(s): 513
DOI: 10.3390/buildings11110513
Abstract:

Data are required for optimizing workplace design, assessing user experience, and ensuring wellbeing. This research focuses on the benefits of incorporating post-occupancy evaluation (POE) data analysis by studying the digital trail of employees generated by the existing Wi-Fi infrastructure of the office. The objective is to enable a safe return to offices through compliance with COVID-19 space-capacity regulations and in consideration of the health and wellbeing of employees. Workplaces, teams, and people have become more digitalized and therefore more mobile due to the globalization of knowledge and cutting-edge technological innovations, a process that has been accelerated by the COVID-19 crisis. Now, hybrid work and fully remote working routines are increasing in a significant number of companies. Nevertheless, with the return to the office, understanding how to calibrate spatial capacity is now key for workplaces and companies. Traditional assessment methods are obsolete; new methods that respond to mobility, changing occupancy rates, and comfort are essential. This paper analyzes, through the case study of a pre-COVID-19 activity-based office, the advantages of using digital indoor-location techniques (such as Wi-Fi networks, which additionally have the advantage of being previously installed in the majority of these spaces). The paper demonstrates that the incorporation of digital POE of user trends enabled a more seamless, accurate, and scalable return to a new normal office work scenario and an improved post-COVID-19 design of workplaces.

Copyright: © 2021 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10639377
  • Published on:
    30/11/2021
  • Last updated on:
    02/12/2021
 
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