@article{32366, keywords = {office buildings, occupant behavior, Machine learning, human building interaction, five factor model, personality type}, author = {Tianzhen Hong and Chien-Fei Chen and Zhe Wang and Xiaojing Xu}, title = {Linking human-building interactions in shared offices with personality traits}, abstract = {

Occupant behavior influences office building energy performance. The level of human-building interactions (HBIs) in shared offices strongly influences building energy use and occupant well-being. This study explored the link between occupant personality types and their behaviors of sharing energy and environment control systems and interactions with their colleagues. Inspired by the Five-Factor Model (FFM), we classified HBI behaviors into four dimensions: willingness to share controlknowledge of controlgroup decision behavior, and adaptive strategies. These four variables can be mapped to the four personality traits proposed by the FFM: agreeableness, openness, extraversion, and conscientiousness. Our cluster analysis identified six behavioral patterns: average (17.7%), reserved (15.3%), environmentally friendly (16.6%), role model (24.2%), self-centered (17.2%), and mechanist (9.0%). We further applied association rules, a widely utilized machine learning technique, to discover how demographics, building-related contextual factors, and perception-attitudinal factors influence HBI behaviors. Country, control feature accessibility, and group dynamics were found to be the three most influential factors that determine occupants’ HBI behaviors. The study provides insights about building design and operation, as well as policy to promote socially and environmentally desirable HBI behaviors in a shared office environment.

}, year = {2020}, journal = {Building and Environment}, volume = {170}, pages = {106602}, month = {01/2020}, issn = {03601323}, doi = {10.1016/j.buildenv.2019.106602}, language = {eng}, }