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Buildings 2019, 9(4), 100; https://doi.org/10.3390/buildings9040100

Article
The Rise of Office Design in High-Performance, Open-Plan Environments
1
Sydney School of Architecture, Design and Planning, The University of Sydney, Sydney, NSW2006, Australia
2
School of Business and Tourism, Southern Cross University, Lismore, QLD4225, Australia
*
Author to whom correspondence should be addressed.
Received: 28 March 2019 / Accepted: 21 April 2019 / Published: 23 April 2019

Abstract

:
This study aimed to identify key drivers behind workers’ satisfaction, perceived productivity, and health in open-plan offices while at the same time understanding design similarities shared by high-performance workspaces. Results from a dataset comprising a total of 8827 post-occupancy evaluation (POE) surveys conducted in 61 offices in Australia and a detailed analysis of a subset of 18 workspaces (n = 1949) are reported here. Combined, the database-level enquiry and the subset analysis helped identifying critical physical environment-related features with the highest correlation scores for perceived productivity, health, and overall comfort of the work area. Dataset-level analysis revealed large-size associations with spatial comfort, indoor air quality, building image and maintenance, noise distraction and privacy, visual comfort, personal control, and connection to the outdoor environment. All high-performance, open-plan offices presented a human-centered approach to interior design, purposely allocated spaces to support a variety of work-related tasks, and implemented biophilic design principles. These findings point to the importance of interior design in high-performance workspaces, especially in relation to open-plan offices.
Keywords:
open-plan offices; post-occupancy evaluation; perceived productivity; satisfaction; design

1. Introduction

Since its adoption by large corporations, open-plan offices have received their fair share of criticism. Anecdotal evidence of the failures of open-plan offices coming from all corners of the industry has accumulated over the decades, and there is little doubt about the polarizing effect that the concept has among workers. Within academia, several research publications have been devoted to the topic, and this number is on the rise—a search on Scopus shows that the number of papers published with “open-plan office” as part of the title, abstract, or keywords in 2018 (n = 60) was 15 times higher than in 1999 (n = 4). When organized by the number of citations, in the top 30 papers published since 1999, the most common focus of investigations in decreasing order was indoor environmental quality (IEQ) (excluding acoustics), acoustics, and way of working.
When it comes to indoor environmental quality (excluding acoustics), the most highly cited papers found on our Scopus search mapped issues around personal control [1], lighting [2,3], exposure to daylight [4], and control systems and technology [5,6,7]. Some of these papers also attempted to understand links between IEQ and satisfaction [8,9,10], performance/perceived productivity [2,11], job satisfaction [12], and energy conservation [13]. Combined, the papers have consolidated a significant body of knowledge about occupants’ dissatisfaction indoors. A combination of methods, including subjective questionnaires and objective measurements in situ, has been deployed when evaluating occupants’ perception and indoor environmental quality performance. Perceived productivity within workspaces has also been extensively documented. What these papers normally overlook is the physical configuration of the space where the data was collected, with open-plan being used as a blanket term to describe workspaces, which has limited the ability to understand how specific interior design features may, if at all, be linked to poor satisfaction results found in subjective and/or objective assessments.
For acoustics, the most highly cited papers were devoted to understanding issues around balancing privacy and communication [14,15], speech intelligibility [16], and predictive models [17], which were noted as well-known weaknesses of open-plan offices. Papers have also been aimed at proposing new measurement methods [18] as well as linking noise with performance [19,20,21] and concentration levels [22] in open-plan offices. Recent research on acoustic-related issues is undoubtedly promising, especially when considering that this IEQ dimension has been strongly linked to major productivity losses in open-plan offices. A move from traditional lab-based experiments to research conducted in situ is also noted, which is necessary, considering the several confounding variables influencing occupants’ perception indoors. Research on partitions and other physical and non-physical barriers to assist with poor-acoustic performance has also been welcomed by academia and industry. On this point, investigating interior design seems like a logical step in this field of research, especially its strategic use to address acoustic-related issues in open-plan offices.
When shifting the attention to the way of working, most highly-cited papers focused on the flexible office [23], configuration of the space [24], employees’ attitudes [25], and coworking [26]. This fascinating field of research, although not new, has been gaining momentum in academia and industry due to the significant changes observed in corporate real estate worldwide over the last decade. Perhaps, out of the three most highly cited papers investigated here, way of working is the topic with stronger links and evidence in terms of the design of offices. That said, traditionally, research published within this field shows a heavy reliance on one-off case studies within one organization, which has limited the possibility of in-depth investigations and generalization of results.
The majority of papers found in this Scopus search point to several shortcomings of open-plan offices, sometimes suggesting solutions to address dissatisfaction. However, only a few have attempted to explore key drivers behind occupants’ satisfaction and how open-plan offices can be improved, if at all, to achieve this goal. With the rapidly increasing numbers of people working in open-plan offices every day around the globe, it is time to focus on harvesting evidence from success stories, with the intention of potentially replicating solutions that have yielded high-satisfaction results. To this end, this study aimed to identify key drivers behind workers’ satisfaction, perceived productivity, and health while at the same time identifying critical physical environment-related features shared by high-performance, open-plan offices. To this end, this paper reports findings from a total of 8827 post-occupancy evaluation (POE) surveys conducted in 61 high-end offices in Australia. This database-level enquiry led to a detailed analysis of a subset of 18 high-performance workspaces (n = 1949). Results from data collected during site visits and fit-out specific features plus floor plan analysis of the offices were also included, providing the context needed to understand design-related choices shared by the subset of high-performance offices. By combining occupant survey responses with fit-out information, this paper aims to push the industry towards workspace design solutions that are adequate for open-plan, high-performance offices.

2. Materials and Methods

This paper presents results from research investigations conducted in Australian open-plan offices under the SHE (Sustainable and Healthy Environments) umbrella. This research platform focuses on how the design of indoor and outdoor environments can be harnessed to deliver satisfaction, health, and productivity. This multidisciplinary platform brings together experts from architecture, IT, and health science to develop collaborative investigations in Australian indoor and outdoor environments.
Under the SHE umbrella and for this paper, POE surveys were conducted with the BOSSA (Building Occupant Survey System Australia) Time-Lapse tool. Developed and managed by The University of Sydney and the University of Technology Sydney, the BOSSA Time-Lapse tool is endorsed by the National Australian Built Environment Rating System (NABERS), Green Building Council of Australia (GBCA), New Zealand Green Building Council (NZGBC), and the WELL Building Standard. Organizations volunteer to use POE surveys, mostly driven by the requirements of these tools.
The POE questionnaire includes background questions addressing participants’ gender, age, type of work, time spent in buildings, workspace arrangement and modules focusing on spatial comfort, individual space, indoor air quality, thermal comfort, noise distraction and privacy, visual comfort, personal control, building image, and overall occupant satisfaction. Workers rate their satisfaction on a seven-point scale (1 = lowest rating; 4 = neutral, and 7 = highest rating). For full questionnaire details, please refer to Reference [27]. The web-based questionnaire takes less than 15 minutes to be completed by occupants.
For this paper, results concentrate on database-level analysis of a total 8827 POE surveys collected from 61 offices. In addition to POE surveys, floor plans and fit-out specific information were also collected from all workplaces investigated, along with site-visits from researchers. Structured notes were taken about the physical configuration of the space, including the presence of use of biophilic concepts and green features, such as vertical gardens and walls. This information aimed to provide the context for the interpretation of results from the POE surveys.
Out of 47 main POE survey questions, 28 were used as input, and 3 were used as output variables for the experimentation. Survey questions used as input variables are based on the work area; spatial, visual comfort, and thermal comfort; individual space; indoor air quality; noise distraction and privacy; personal control; connection to outdoor environment; and building image and maintenance. The output variables are the general survey questions on perceived productivity, health, and overall comfort of the work area. Table 1 lists all the 31 variables used in this work.
The best-performing offices regarding perceived productivity, health, and overall comfort were then identified for a more in-depth analysis. As a result, findings from a subset of 1949 POE surveys from 18 offices are also reported here, and necessary information about this subset is presented in Table 2. This subset features premium spaces, holding certifications from the Green Building Council of Australia (GBCA) and/or WELL Building Standard. Offices are located in buildings that hold a valid rating from the National Australian Built Environment Rating System (NABERS), which is typical to high-end corporate real estate in Australia. Tenants organizations are from the property industry, finance, government, design, and consultancy sectors. The majority of offices from the subset of 18 are open-plan, and 4 were designed to support activity-based working. All POE surveys were conducted at least 6 months after relocation and were mostly driven by GBCA’s rating requirements. Table 2 shows basic information about the surveyed offices, comprising the subset featuring in the workspace ranking.

2.1. Statistical Analysis

2.1.1. Pre-Processing

Pre-processing involved replacing missing instances and discarding invalid instances. We represented the matrix with 28 input variables (i.e., features) as Xm×28 = [x1, …, x28], where xi represents each feature and m is the number of instances/observations in xi. Similarly, Ym×3 = [y1, …, y3] denotes the matrix of 3 output (y) variables. Any missing instance in each feature xi (e.g., jth instance of xi is xj,i) is estimated using a linear interpolation between the two adjacent instances (i.e., x j-1,i and xj+1,i).
Data (instances) from the workspaces with a sample size less than 20 were not considered in this experiment. The following steps were conducted for each output variable, and each time; instances from X X (e.g., instances at jth position in Xj,i) were discarded where the corresponding instance of output variable (i.e., yj,i) was ‘null.’ This last step resulted in different sample sizes for different output variables, i.e., productivity, health, and overall comfort of the work area.

2.1.2. Correlation-Based Feature Ranking

The first goal of the experiment was to identify which features were most strongly associated with the output variables. A correlation between input and output variables can identify the degree of association between them. A two-sided Pearson correlation coefficient is computed between each feature, xi and each output variable, yj. A Pearson correlation coefficient, ρx,y is computed with (1), where cov (xi, yj) is the covariance of (xi, yj) and σ is the standard deviation of them. The feature matrix X is sorted into a descending order (i.e., X′ = [xp, …, xq, …, xr: yj,p ≥ yj,q ≥ yj,r]) with respect to ρx,y values obtained for each yj. A list of abbreviations is provided in Table 1, including the full questions of the POE survey.
ρ x i , y j = cov ( x i , y j ) σ x i σ y j

2.1.3. Statistical Difference

Wilcoxon rank-sum (WRS) test determines if two independent samples originate from populations with the same distribution. A WRS test is nonparametric, as it does not assume that the samples belong to a known (i.e., normal) distribution [28]. Samples A and B were created for each output variable from the instances in X using the scores/ratings. Instances in X that corresponded to the ratings between 1 and 3 in a particular output variable (yj,k) were grouped into A. Similarly, ratings in yj,k between 4 and 7 were used to group the corresponding instances of X into B, as shown in Equations (2) and (3).
A = [ F j , i = 1 : 28 : A X ,   y j , k 1     y j , k 3   ] , k = 1 : 3
B = [ F j , i = 1 : 28 : B X ,   y j , k 4     y j , k 7   ] , k = 1 : 3
A two-sided WRS test was then conducted for each pair of A and B for each yj,k = 1:3 with a null hypothesis stating that the data in A and B belong to distributions with equal medians, against the alternative hypothesis that they do not with a significance level α = 0.05. The test returns a p-value and h-value, where h = 1 indicates a rejection of the null hypothesis and h = 0 indicates rejection of the alternative hypothesis with a 5% significance level. The test p-h values were calculated with Equation (4).
( p , h ) = WRS ( A , B )

2.1.4. Classification-Based Feature and Workspace Ranking

Forward feature selection (FSS) is a machine learning based feature selection approach that can rank many features predicting a particular output variable. FSS selects a subset of features in X that best predict the output variable. FSS starts with no feature and keeps adding features sequentially until the prediction performance stops improving [29]. The following procedure was applied to each output variable yi = 1:3. A ground-truth was computed for each output variable (yi = 1:3) using the (5) ratings between 1 and 7 as follows:
y j , i = { 0 ,     y j , i 1     y j , i 3 1 ,     y j , i 4     y j , i 7
The FSS uses k-fold cross-validation (k = 10) while selecting the candidate features, to randomly split the instances of X and yi into 10 equal-sized disjoint subsamples. The FSS trains an SVM classifier and predicts a particular output variable for each subsample. This process is iterated, and each time a feature that has not been selected yet is added. The outcome of this process is a set of selected features with a set of criterion values. The criterion value is an estimation of the mean miss-classification rate, and the algorithm keeps adding features until there is no decrease in the criterion value. The selected features are considered to achieve higher classification accuracy than the rest of the features in X [29]. We represented the subset of selected features as X′, where X′ ⸦ X and X′ = (x1, …, xn): n < 28 (i.e., X′ should have lesser number of features than X).
The criterion values for each selected feature were used as ‘weight’ to obtain a ranking of the workspace. A dot multiplication was computed between the criterion values for each selected feature and the instances of that feature. The multiplication outcome was separated for each workspace, and a mean was taken to compute a raking score for each workspace. The workspaces were then sorted according to this ranking score. The W = (w1, …, wt) (t = number of workspaces) can be considered as a list of the ranked workspace.
A similar feature selection was conducted using the divided subsamples from X and Y. The ‘office layout’ feature was used to separate both X and Y into two separate subsamples: ‘open-plan’ and ‘private’. An identical FFS-based feature ranking approach provided two lists of best-performing features for each output variables, along with respective criterion values.

2.1.5. Analyzing Top-Ranked Workspaces

The classification-based feature selection provided a subset of features (i.e., X′) that best-predicts each output variable (i.e., y1:4). A list of ranked workspaces was then obtained for each output variable from the mean criterion scores of these features. Each feature, Fi contains a number of instances, namely satisfaction/agreement (score 5–7) and dissatisfaction/disagreement (score 1–3) scores. These measures do not incorporate the neutral scores (i.e., score 4). Fractions of satisfaction/agreement and dissatisfaction/disagreement scores were computed for the top four selected features for the four highest ranked workspaces, using Equations (6) and (7). These two measures indicate the overall rate of satisfaction/agreement and dissatisfaction/disagreement for each feature in each workspace and each output variable. This procedure was iterated for four output variables including productivity, health, overall comfort, and overall building.
Fraction satisfacion _ score = Number   of   satisfaction   scores   in   x i Total   number   of   instances   x i × 100 %
Fraction dissatisfaction _ score = Number   of   dissatisfaction   scores   in   x i Total   number   of   instances   in   x i × 100 %

2.1.6. Overall Satisfaction Scores

The selected features in X′ for the top four workspaces were combined to form a list of best-performing features. A mean of the instances of each feature in X′ was computed for each of the top-ranked workspaces for each output variable. This experiment was further extended by taking a similar mean of the instances of each feature in X′ for the ‘open-plan’ and the ‘private’ workspaces, regardless of the output variables and any particular workspace.

3. Results

3.1. High Performance Features at the Dataset Level

Table 3 presents 28 features (in descending order) along with the Pearson correlation coefficients. The order of features displayed in this table changes based on the correlation coefficients found for perceived productivity, health, and satisfaction with the overall comfort of the work area. This dataset-level enquiry shed light on key features shared by open-plan offices and facilitated the subsequent mapping of high-performance workspaces. Interestingly, although in different order of importance, the features depicted in Table 3 and Figure 1 show a strong link with the impact of interior design on the performance of these spaces according occupants’ subjective assessments reported on POE surveys.
For perceived productivity, the features presenting large-size associations (ρ > 0.50) were six in total, including work area aesthetics, distraction/unwanted interruption, overall amount of noise, furnishing, building aesthetics, and space to collaborate. For health, questionnaire items presenting large-size associations (ρ > 0.50) were seven, namely air quality, work area aesthetics, air movement, building aesthetics, access to daylight, furnishing, and space for breaks. For comfort of the workspace, questionnaire items presenting large-size associations were seventeen in total: furnishing, work area aesthetics, air quality, building aesthetics, air movement, degree of adaptation, space for breaks, humidity, cleanliness, maintenance, connection to outdoors, interaction with colleagues, space for collaboration, lighting, noise, personalization of work area, and amount of space. As depicted in Figure 1, when combined, large-size associations were mostly concentrated on questionnaire items linked with seven key dimensions, namely spatial comfort (six features), indoor air quality (three features), building image and maintenance (three features), noise distraction and privacy (two features), visual comfort (one feature), personal control (one feature), and connection to the outdoor environment (one feature).
When combined, results from Table 3 and Figure 1 clearly point to the importance and opportunities of exploiting interior design to address occupants’ dissatisfaction in open-plan offices. Work area aesthetics was highly ranked in all three dimensions investigated here, which is undoubtedly a domain driven by interior design. What is interesting about this result is that work area aesthetics has not been traditionally considered or investigated in research conducted in open-plan offices. Similarly, comfort of furnishing and degree of freedom to adapt the normal work area have also appeared prominently for all three dimensions investigated here. These results suggest that specifications for overall layout, zoning, and furniture should be carefully considered when designing open-plan offices.

3.2. High-Performance Features for Open-Plan and Private Offices

Table 4 lists the ranking of the best-performing features that predicted perceived productivity, health, and overall comfort of the work area for open-plan offices. The subset is considered as the best-performing feature subset among all 28 features in X. The number of features obtained for each output variable varies as the iteration feature selection breaks over the condition on classification performance. Figure 2 and Figure 3 depict the best-performing features for predicting perceived productivity, health, and overall comfort of work area per dimension and office typology.
For open-plan offices, the best-performing features for predicting perceived productivity were a total of seven: amount of interruption, work area aesthetics, degree of adaptation of the work area, furnishing, overall amount of noise, cleanliness, and personal control over lighting. Furnishing, work area connection to outdoors, building aesthetics, sound privacy, and degree of adaptation of the work area were the critical predictors of health. As for the overall comfort of the work area, six features were key predictors, namely work area aesthetics, degree of adaptation of the work area, furnishing, overall air quality, cleanliness, and amount of interruption. As depicted in Figure 2, critical predictors in open-plan offices can be linked to the spatial comfort of the work area, indoor air quality, noise distraction and privacy, personal control, connection to the outdoor environment, and building image and maintenance. Table 4 shows the ranking of best-performing features of open-plan offices for predicting perceived productivity, health, and overall comfort of the work area. Figure 2 shows the best-performing features of open-plan offices for predicting perceived productivity, health, and overall comfort of work area.
For private offices, the best-performing features for predicting perceived productivity were the amount of interruption, sound privacy, interaction with colleagues, and overall air quality. For health, the key predictors were overall air quality, humidity, and overall maintenance building. As for the overall comfort of the work area, four features were key predictors, namely degree of adaptation of the work area, furnishing, interaction with colleagues, and overall amount of noise. As depicted in Figure 3, critical predictors in private offices can be linked to the spatial comfort of the work area, indoor air quality, noise distraction and privacy, personal control, and building image and maintenance. Table 5 shows the ranking of best performing features for private offices and Figure 3 shows the best-performing features for predicting perceived productivity, health, and overall comfort work area.

3.3. High-Performance Workspaces

Table 6 includes the rates (fractions) of satisfaction and dissatisfaction scores for the top-ranked workspaces for perceived productivity, health, and overall comfort. The fractional scores of the top workspaces were higher (>50%) for either satisfaction or dissatisfaction scores for the high-performing features for each output variable. This signifies that these high-performing features had a good correlation with the output variables and were selected during the classification-based feature selection.
Table 7 includes the mean satisfaction scores for the top workspaces in terms of perceived productivity, health, and overall comfort. The mean satisfaction scores fell between 4 and 6 (on a 7-point scale), which indicates that these features obtained high satisfaction scores overall.

4. Discussion

Dataset- and feature-level analysis show that the spatial comfort of the work area is key for predicting workers’ satisfaction, as confirmed by the results reported in References [5,6,7,8,9]. The physical configuration of highly-ranked offices supports this finding, as their interior design privileged zoning and the implementation of a variety of spaces to support different activities during the day. These spaces had several zones intentionally allocated for breaks, collaboration, concentration, and private conversations. As a result, it is not surprising that satisfaction results from these offices were significantly higher regarding the amount of interruption and sound privacy—well-known issues of open-plan offices and also important predictors found here for perceived productivity, health, and satisfaction with the overall work area. This is an important finding considering the ever-challenging balance between collaboration and acoustics-related issues observed on open-plan offices. Investing in designs that provide workers with a variety of zones within open-plan offices will allow them to more efficiently develop different work-related activities that require concentration, privacy and/or interaction with others. This is a key move in mitigating acoustic-related issues in open-plan offices and should be carefully considered by designers.
In addition, high-performance workspaces presented high scores on key predictive features, namely overall aesthetics of the work area, comfort of furnishings, degree of freedom to adapt, and connection to outdoors. Once again, these aspects are related to the interior design of offices. Analysis of the physical configuration of these offices showed that their design predominantly embraced organic shapes intended to bring spaces together without visual barriers. When used, partitions employed glass and textured elements of plants. Pods of all sizes were also a prominent in these spaces and had walls with textured elements and/or plants, promoting visual integration but some privacy at the same time. The sense of spaciousness was also enhanced by the use of large voids, sometimes of the size of atriums and/or staircases. In addition, the design of these offices has also placed strong care on furniture ergonomics and presence of sit-stand workstations. The vast majority of offices also had workstations located near the façade, which allowed direct access to a view. These workstations are intended for temporary use, so no workers are permanently based there. Finally, the design of offices investigated here clearly embraced biophilic principles. Overall, layouts privileged workers’ access to daylight and views, locating workstations on the perimeter zones of the space. Green walls and other features were also consistently observed in several zones, enhancing workers’ exposure to nature.

5. Conclusions

This paper presented dataset-level analysis of a total of 8827 post-occupancy evaluation (POE) surveys conducted in 61 high-end offices in Australia and a detailed analysis of a subset of 18 high-performance workspaces (n = 1949). In addition to surveys, structured site visits and floor plans were reported here. When merged, these analyses allowed identification of critical features and physical configuration of offices highly ranked in terms of perceived productivity, health, and overall satisfaction with the work area.
Dataset-level analysis revealed large-size associations with spatial comfort (six features—space for breaks, work area aesthetics, interaction with colleagues, personalization of work area, space to collaborate, and comfort of furnishing), indoor air quality (three features—air movement, humidity, and overall indoor air quality), building image and maintenance (three features—cleanliness work area, overall maintenance building, and building aesthetics), noise distraction and privacy (two features—unwanted interruptions and overall noise work area), visual comfort (one feature—lighting comfort work area), personal control (one feature—degree of freedom to adapt work area), and connection to the outdoor environment (one feature—sense of connection work area and outdoor). For open-plan offices, critical predictors can be narrowed to spatial comfort of the work area, indoor air quality, noise distraction and privacy, personal control, connection to the outdoor environment, and building image and maintenance. For private offices, the critical predictors found are linked to the spatial comfort of the work area, indoor air quality, noise distraction and privacy, personal control, and building image and maintenance.
All offices with very high results for perceived productivity, health, and overall comfort of the work area were highly ranked in our analysis: a human-centered approach to interior design purposely allocated spaces to support a variety of work-related tasks and implemented biophilic design principles. These findings point to the importance of interior design in high-performance workspaces, especially when it comes to open-plan offices.

Author Contributions

The authors contributed to the paper in the following way: conceptualization, C.C., formal analysis, P.C., C.C. and D.T.; Writing—Original Draft preparation, C.C., P.C. and D.T., Funding: C.C. and D.T.

Funding

This research was funded by the University of Sydney’s DVC Research Bridging Support Grant (G199771) and Cachet Group (G192167).

Acknowledgments

The authors would like to express their gratitude to all organizations and occupants for dedicating their time to participate in this study.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Large-size correlation associations for perceived productivity, health, and overall comfort work area found in the entire dataset.
Figure 1. Large-size correlation associations for perceived productivity, health, and overall comfort work area found in the entire dataset.
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Figure 2. Best-performing features of open-plan offices for predicting perceived productivity, health, and overall comfort of work area.
Figure 2. Best-performing features of open-plan offices for predicting perceived productivity, health, and overall comfort of work area.
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Figure 3. Best-performing features of private offices for predicting perceived productivity, health, and overall comfort work area.
Figure 3. Best-performing features of private offices for predicting perceived productivity, health, and overall comfort work area.
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Table 1. BOSSA (Building Occupant Survey System Australia) Time-Lapse post-occupancy evaluation (POE) questionnaire items used as input and output (first 3) variables.
Table 1. BOSSA (Building Occupant Survey System Australia) Time-Lapse post-occupancy evaluation (POE) questionnaire items used as input and output (first 3) variables.
TypeAbbreviationVariablesQuestions
General-Perceived productivity (Output variable)How does your work area influence your productivity?
-Health (Output variable)How does your work area influence your health?
-Overall comfort of the work area (Output variable)All things considered, how satisfied are you with the overall comfort of your normal work area?
Work areaOLOffice layoutWhich of the following best describes your normal work area? 1–2: private office, 3–4: open-plan office, 6: other.
Spatial comfortSBSpace for breaksThis building provides pleasant spaces (e.g., indoor or outdoor green space, break-out areas) for breaks and relaxation.
WAWork area aestheticsPlease rate your satisfaction with the visual aesthetics of your normal work area.
ICInteraction with colleaguesHow do you rate your normal work area’s layout in terms of allowing you to interact with your colleagues?
PEPersonalization of work areaMy normal work area can be adjusted (or personalized) to meet my preferences.
SCSpace to collaborateThe building provides adequate formal and informal spaces to collaborate with others.
FUComfort of furnishingPlease rate how comfortable your work area’s furnishings are (including chairs, desk, equipment, and so on).
Individual spaceASAmount of workspacePlease rate your satisfaction with the amount of space available to you in your normal work area.
STStorage spacePlease rate your satisfaction with the amount of personal storage space available to you.
Indoor air qualityAIAir movementPlease rate your satisfaction with the air movement available to you in your normal work area.
HUHumidityPlease rate your satisfaction with the overall humidity in your normal work area.
AQAir qualityPlease rate your satisfaction with the overall air quality in your work area.
Noise distraction & privacyINUnwanted interruptionThe work area’s layout enables me to work without distraction or unwanted interruptions.
VPVisual privacyMy normal work area provides adequate visual privacy (not being seen by others).
SPSound privacyMy normal work area provides adequate sound privacy (not being overheard by others).
NONoisePlease rate your satisfaction with the overall noise in your normal work area.
Visual ComfortLILightingPlease rate your satisfaction with the lighting comfort of your normal work area (e.g., amount of light, glare, reflections, contrast)?
SHShadingPlease rate your satisfaction with shading devices (blinds, curtains, and so on) in terms of controlling unwanted glare?
Personal controlPHPersonal control heating/coolingHow do you rate the level of personal control over the heating or cooling of your normal work area?
PAPersonal control air movementHow do you rate the level of personal control over the air movement of your normal work area?
PLPersonal control lightingHow do you rate the level of personal control over the artificial lighting in your normal work area?
ADDegree of freedom to adaptAll things considered, how satisfied are you with the degree of freedom to adapt your normal work area (air-conditioning, opening the window, lighting, and so on) to meet your preferences?
Connection to the outdoor environmentVIExternal viewPlease rate your satisfaction with the external view from your normal work area.
ADAccess to daylightPlease rate your satisfaction with access to daylight from your normal work area.
COConnection to outdoorsThis building provides a sense of connection between my normal work area and the outdoor environment.
Building image & maintenanceCLCleanlinessPlease rate your satisfaction with the general cleanliness of your normal work area.
MAMaintenancePlease rate your satisfaction with the general maintenance of this building.
BABuilding aestheticsPlease rate the overall visual aesthetics of this building.
Table 2. Basic information about surveyed offices.
Table 2. Basic information about surveyed offices.
IDSample Size (n = 1949)Response Rate (%)TenantTenant CertificationOffice Layout Way of Working
120-Property industryGBCA*Open planFixed location
280513FinanceGBCAOpen planNon-fixed location (activity-based working)
33253Property industryGBCAPrivate and Open planFixed location
428-Property industryGBCAOpen planNon-fixed location (activity-based working)
53949Property industryGBCAOpen planFixed location
616032GovernmentGBCAPrivate and Open planFixed location
711245Property industryGBCAPrivate and Open planFixed location
83289Design & ConsultancyGBCAPrivate and Open planFixed location
95162Property industryGBCAOpen planFixed location
1015025Government-Open planFixed location
112255Property industryGBCAPrivate and Open planFixed location
125663ConsultancyGBCAOpen planNon-fixed location (activity-based working)
132980Property industryGBCAPrivate and Open planFixed location
1445-Property industryGBCAOpen planFixed location
1516120Property industryGBCA and WELLOpen planNon-fixed location (activity-based working)
1610542Property industryGBCA and WELLOpen planFixed location
177551Property industryGBCAOpen planFixed location
182761Property industryGBCAOpen planFixed location
* Green Building Council of Australia.
Table 3. Pearson correlation coefficient (ρ) computed for: (a) perceived productivity; (b) health; (c) overall comfort of the work area. The 28 features are sorted in descending order according to the value of ρ.
Table 3. Pearson correlation coefficient (ρ) computed for: (a) perceived productivity; (b) health; (c) overall comfort of the work area. The 28 features are sorted in descending order according to the value of ρ.
(a)(b)(c)
ProductivityHealthOverall Comfort of the Work Area
Work area aesthetics0.57Air quality0.55Comfort of furnishing0.65
Unwanted interruption0.53Work area aesthetics0.54Work area aesthetics0.65
Noise0.52Air movement0.52Air quality0.63
Comfort of furnishing0.52Building aesthetics0.51Building aesthetics0.61
Building aesthetics0.51Degree of freedom to adapt0.51Air movement0.61
Space to collaborate0.5Comfort of furnishing0.51Degree of freedom to adapt0.58
Space for breaks0.49Space for breaks0.5Space for breaks0.56
Degree of freedom to adapt0.49Connection to outdoors0.49Humidity0.56
Air quality0.49Space to collaborate0.48Cleanliness0.56
Connection to outdoors0.48Humidity0.47Maintenance0.56
Interaction with colleagues0.48Maintenance0.46Connection to outdoors0.55
Air movement0.48Cleanliness0.45Interaction with colleagues0.54
Personalization of work area0.46Lighting0.43Space to collaborate0.54
Sound privacy0.45Personalization of work area0.42Lighting0.53
Maintenance0.44Unwanted interruption0.42Noise0.51
Cleanliness0.44Noise0.42Personalization of work area0.50
Lighting0.44Interaction with colleagues0.42Amount of workspace0.50
Amount of workspace0.43External view0.41External view0.50
Humidity0.42Sound privacy0.39Unwanted interruption0.48
External view0.41Access to daylight0.39Access to daylight0.48
Visual privacy0.41Shading0.36Shading0.46
Access to daylight0.38Personal control heating/cooling0.35Sound privacy0.41
Shading0.36Personal control air movement0.35Storage space0.41
Storage space0.35Amount of workspace0.35Visual privacy0.39
Personal control heating/cooling0.31Visual privacy0.34Personal control heating/cooling0.31
Personal control air movement0.31Personal control lighting0.32Personal control air movement0.31
Personal control lighting0.30Storage space0.29Personal control lighting0.30
Table 4. Ranking of best-performing features of open-plan offices for predicting perceived productivity, health, and overall comfort of work area.
Table 4. Ranking of best-performing features of open-plan offices for predicting perceived productivity, health, and overall comfort of work area.
Productivity.HealthOverall Comfort Work Area
InterruptionAir qualityWork area aesthetics
Work area aestheticsFurnishingAdaptation
AdaptationWork area connection to outdoorsFurnishing
FurnishingBuilding aestheticsAir quality
NoiseSound privacyCleanliness
CleanlinessAdaptationInterruption
Personal control lighting
Table 5. Ranking of best-performing features of private offices for predicting perceived productivity, health, and overall comfort of work area.
Table 5. Ranking of best-performing features of private offices for predicting perceived productivity, health, and overall comfort of work area.
ProductivityHealthOverall Comfort of Work Area
InterruptionAir qualityAdaptation
Sound privacyHumidityFurnishing
Interaction with colleaguesMaintenanceInteraction with colleagues
Air quality Noise
Table 6. Rate of satisfaction and dissatisfaction scores for (a) perceived productivity, (b) health, and (c) overall comfort work area.
Table 6. Rate of satisfaction and dissatisfaction scores for (a) perceived productivity, (b) health, and (c) overall comfort work area.
Workspace IDEJKO
Rate (%) of Satisfaction (SAT)/Dissatisfaction (DIS) ScoresSATDISSATDISSATDISSATDIS
Interruption7525662263296427
Work area aesthetic3055940902982
Sound privacy4050314723635039
Personalization5040873904897
(a) Productivity
Workspace IDDJOP
Rate (%) of Satisfaction/Dissatisfaction ScoresSATDISSATDISSATDISSATDIS
Air quality960943952894
Furnishing897816935915
Connection outdoors7514877877848
Building aesthetics10001000980980
(b) Health
Workspace IDJKOP
Rate (%) of Satisfaction/Dissatisfaction ScoresSATDISSATDISSATDISSATDIS
Work area aesthetics940902982906
Amount of space916888962887
Adaptation5028216148314036
Maintenance913846962934
(c) Overall comfort
Table 7. Mean satisfaction scores for (a) perceived productivity, (b) health, and (c) overall comfort of work area.
Table 7. Mean satisfaction scores for (a) perceived productivity, (b) health, and (c) overall comfort of work area.
Workspace IDEJOK
Work area aesthetics3.26.096.455.92
Furnishing4.65.7265.67
Amount of space4.96.036.365.82
Humidity4.96.1664.69
Air quality4.156.096.114.63
Interruption5.15.034.934.57
Sound privacy3.53.664.183.12
Adaptation2.94.444.413.29
Connection outdoors2.65.846.025.69
Cleanliness4.056.196.465.92
Building aesthetics2.46.346.365.45
(a) Productivity
Workspace IDOJDP
Work area aesthetics6.456.095.825.94
Furnishing65.725.866.07
Amount space6.366.036.145.91
Humidity66.166.075.83
Air quality6.116.096.045.95
Interruption4.935.033.964.42
Sound privacy4.183.663.073.2
Adaptation4.414.4443.98
Connection outdoors6.025.845.255.75
Cleanliness6.466.1966.11
Building aesthetics6.366.346.366.46
(b) Health
Workspace IDOJPK
Work area aesthetics6.456.095.945.92
Furnishing6.005.726.075.67
Amount space6.366.035.915.82
Humidity6.006.165.834.69
Air quality6.116.095.954.63
Interruption4.935.034.424.57
Sound privacy4.183.663.203.12
Adaptation4.414.443.983.29
Connection outdoors6.025.845.755.69
Cleanliness6.466.196.115.92
Building aesthetics6.366.346.465.45
(c) Overall comfort

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