Establishing a Conceptual Framework Predicting Occupant Satisfaction By Systematic Poes

Jinoh Park (North Carolina State University)

This research conceptualizes a new Post-Occupancy Evaluation (POE) methodology utilizing statistics to predict occupant satisfaction in the CCRCs for sustainable development. The POE has been limited in outlining generalizable or objective evidence, favoring context-specified outcomes; this methodology stands on context-specific, evidence-based design tools instead of a rigorous foundation to establish generalizable findings (Anderzhon et al., 2007; McLaughlin et al., 2014). Statistics is used widely not only in object-oriented disciplines such as science and engineering but also in subject-oriented disciplines such as social sciences. The reliability of a statistical approach, especially on subjective matters, has been proven in historically subjective areas such as measuring wellness (Krueger & Schkade, 2008) or happiness (Kőszegi & Rabin, 2008). Even though all statistical and actual values might not be matched (Mincey, 2006), statistically significant results can be close to the actual values. Therefore, using statistical approaches can be a meaningful way to prove subjective matters. By applying a statistical approach to the POE, this research aims to 1) address the generalizable limitations of the POE methodology, 2) conceptualize a new POE methodology with statistical models, and 3) verify the conceptualized methodology through the CCRC built environment. The research consists of: 1) collecting previously published articles of POE in CCRCs, 2) establishing systematic criteria for elements meeting occupant satisfaction based on the collected POEs, 3) applying principles of two statistics rules of “The One in Ten Rule (Green, 1991)” and “The Rule of Thirty (Ellis, 2010),” and 4) developing a POE protocol utilizing the statistics rules to investigate the identified elements. Finally, this article will propose a POE protocol for the CCRC built environment. As a result, the protocol allows design practitioners to predict occupant satisfaction in CCRC built environments, then the design practitioners can propose CCRC built environments designed to help developers manage CCRC reliably.