DOI: 10.5061/dryad.r2280gbc4
title: Differential perceptions of teamwork, focused work and perceived productivity as an effect of desk characteristics within a workplace layout

description: The impact of the physical workplace on behaviors and attitudes at work is
 a much-studied topic. Major research streams over the last decades
 investigated either satisfaction with offices in relation to physical
 comfort, or how layout decisions influenced interaction and collaboration
 in the workplace with a focus on open-plan offices. Rather little is known
 on the effect a workplace layout (such as its openness) has on perceptions
 of staff regarding teamwork, focused work and perceived productivity. We
 aim to close this gap by taking a differential approach which appreciates
 detailed variations within open-plan offices. Not every corner of an
 office is the same, so the question arises whether satisfaction with
 workspace differs depending on where someone is sitting. Bringing results
 of a staff survey in the UK headquarters of a global technology company
 together with a detailed analysis of spatial qualities at desks based on
 isovist and visual field analysis, we find that staff are less likely to
 rate their workplace environment favorably when they have higher numbers
 of desks within their own field of vision; and when they are facing away
 from the room with a relatively larger area behind their back compared to
 the area surrounding them. Aspects of teamwork that are negatively
 affected include sharing information with others, as well as team identity
 and cohesion. Focused work (concentration) and working productively are
 impacted even more so with the largest effect sizes throughout. These
 findings highlight the relevance of investigating detailed spatial
 qualities of micro-locations in workplace layouts. Our results also raise
 important questions regarding the current popular practice in workplace
 design of providing large open-plan offices for technology companies.
description: Fieldwork was undertaken in spring 2018 in the UK headquarter of
 a global technology company. In the study we collected floor plans of the
 office (including the detailed desk numbers and seating position of staff)
 as well as responses to a workplace satisfaction survey. Participants were
 asked to rate how much the working environment supported or inhibited
 their working life, for instance regarding sharing of information with
 colleagues, team identity and cohesion, concentration and productive work.
 167 responses were obtained. Based on space syntax as a
 method and the concept of an isovist, in the analysis we calculated visual
 field information from the seats of participants in the office, e.g. how
 many desks someone is directly surrounded by (covering a full 360 degree
 view from someone's desk), how many desks are directly visible in
 someone's forward facing vision, etc. We also classified desks by
 type according to their micro-location, e.g. next to a window, corridor,
 wall or mid row. These spatial characteristics of a seat were matched with
 survey responses in order to answer our research question whether office
 workers perceive teamwork, focused work and productivity differently
 according to the spatial features of their desk. We investigate this
 relationship between desk features and job satisfaction using multiple
 ordinal regression models. Due to restrictions on data
 sharing by our Ethics Committee, we cannot share survey responses but the
 repository contains floor plans (in dxf format) as well as the R script we
 used to undertake the analysis. This allows researchers to replicate our
 method with their own data sets.
description: To replicate our analysis, the following steps are
 required: Prepare a dxf file of a floor
 plan (using the floor plan we have provided as an example)
 Extract spatial information from the dxf file in R using the R
 package dxfspatial. See:
 https://github.com/pklampros/dxfspatial Calculate
 isovist properties (for example area of 360 degree isovists from all
 seats) using the R package rdepthmap. See:
 https://github.com/pklampros/rdepthmap Collect data
 from a workplace survey including seating positions of respondents (see
 file survey_questions.pdf) Bring spatial metrics and
 survey responses together in a single csv file (following the structure in
 the file compact-data.csv, which contains a single made up sample
 answer) Use the R script provided as R Markdown
 (generators_rev1.Rmd) to run the analysis.