BOSSA: A multidimensional post-occupancy evaluation tool

Candido, C; Kim, J; De Dear, R; Thomas, L

BOSSA: A multidimensional post-occupancy evaluation tool

Candido, C Kim, J De Dear, R Thomas, L

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Publication Type:: Journal Article
Citation:: Building Research and Information, 2016, 44 (2), pp. 214 - 228
Issue Date:: 2016-02-17

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Field	Value	Language
dc.contributor.author	Candido, C	en_US
dc.contributor.author	Kim, J	en_US
dc.contributor.author	De Dear, R	en_US
dc.contributor.author	Thomas, L https://orcid.org/0000-0003-0707-9260	en_US
dc.date.issued	2016-02-17	en_US
dc.identifier.citation	Building Research and Information, 2016, 44 (2), pp. 214 - 228	en_US
dc.identifier.issn	0961-3218	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122450
dc.description.abstract	© 2015 Taylor & Francis. Research findings point to three methodological shortcomings of current post-occupancy evaluation (POE) tools: (1) contextualizing results, (2) adding instrumental data side by side to survey results and (3) producing meaningful feedback to its key stakeholders. This paper introduces the holistic BOSSA (Building Occupants Survey System Australia) and tools developed under this project's scope in close collaboration with industry. It aims to present and discuss the statistical analysis used in the BOSSA tool, distilling the survey results down to nine indoor environmental quality (IEQ) dimensions and their association with four overall indices. Principal component analysis (PCA) extracted nine IEQ dimensions that were uncorrelated with each other: spatial comfort, indoor air quality, personal control, noise distraction and privacy, connection to the outdoor environment, building image and maintenance, individual space, thermal comfort, and visual comfort. Four separate multiple regression analyses were conducted, one for each global evaluation item as an independent variable: work area comfort, building satisfaction, productivity and health. This statistical analysis provided the rational basis of BOSSA's scoring system, designed to simplify how occupant survey results are communicated to key stakeholders from the property industry and researchers.	en_US
dc.relation	http://purl.org/au-research/grants/arc/LP110200328
dc.relation.ispartof	Building Research and Information	en_US
dc.relation.isbasedon	10.1080/09613218.2015.1072298	en_US
dc.subject.classification	Building & Construction	en_US
dc.title	BOSSA: A multidimensional post-occupancy evaluation tool	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	44	en_US
utslib.for	1201 Architecture	en_US
utslib.for	1202 Building	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Architecture
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	44	en_US

Abstract:

© 2015 Taylor & Francis. Research findings point to three methodological shortcomings of current post-occupancy evaluation (POE) tools: (1) contextualizing results, (2) adding instrumental data side by side to survey results and (3) producing meaningful feedback to its key stakeholders. This paper introduces the holistic BOSSA (Building Occupants Survey System Australia) and tools developed under this project's scope in close collaboration with industry. It aims to present and discuss the statistical analysis used in the BOSSA tool, distilling the survey results down to nine indoor environmental quality (IEQ) dimensions and their association with four overall indices. Principal component analysis (PCA) extracted nine IEQ dimensions that were uncorrelated with each other: spatial comfort, indoor air quality, personal control, noise distraction and privacy, connection to the outdoor environment, building image and maintenance, individual space, thermal comfort, and visual comfort. Four separate multiple regression analyses were conducted, one for each global evaluation item as an independent variable: work area comfort, building satisfaction, productivity and health. This statistical analysis provided the rational basis of BOSSA's scoring system, designed to simplify how occupant survey results are communicated to key stakeholders from the property industry and researchers.

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http://hdl.handle.net/10453/122450