A data-driven analysis of occupant workspace dissatisfaction. (November 2021) - Kent, Michael; Parkinson, Thomas; Kim, Jungsoo; Schiavon, Stefano
Abstract: Studies often aim to determine which indoor environmental quality parameters best predict the overall workspace assessment. However, this method overlooks important differences distinguishing satisfied and dissatisfied occupant groups. We used a new analytical approach on 36671 post-occupancy evaluation responses to overcome this problem and better understand workspace satisfaction in office buildings. Principal components analysis reduced satisfaction votes with 15 different IEQ items into two principal components related to: 1) privacy and amount of space, and 2) cleanliness and maintenance. We grouped the data by occupants that were either satisfied or dissatisfied with their workspace. Principal component 1 explained half of the variability in the dataset and reliably distinguished occupants satisfied with their workspace from those that were dissatisfied. We used support vector machine to classify the satisfied and dissatisfied groups based on principal components 1 and 2. Classification of occupant satisfaction with the overall workspace was highly accurate (approximately 90%) and based predominantly on the component related to privacy and amount of space. Further analyses showed that occupants satisfied with their overall workspace were generally satisfied with all other IEQ items. There was greater independence between workspace attributes for those dissatisfied with their overall workspace. Issues of privacy and available space were an overwhelming determinant of occupant dissatisfaction irrespective of the success of other workspace attributes. These findings suggest that efforts to improve occupant satisfaction with workspaces should leverage designs that ensure privacy and provide sufficient space to support occupants in their work. Graphical abstract: Image 1 Highlights: Workspace evaluations from 36671 satisfied or dissatisfied occupants were compared. A single principal component (privacy and space) explained half of the data variance. Two principal components correctly classified ~90% of overall workspace satisfaction votes. Dissatisfaction with privacy and space heavily influences overall workspace satisfaction. Feedback from dissatisfied occupants is useful for building diagnostics and operations.