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• W4220700805 abstract "Research on the drivers of energy-related behaviors in buildings has gained significant interest in recent years. However, existing studies are often limited to a restricted scope of analysis (e.g., single-domain and single-cultural contexts) and rely on simplistic data analysis tools that may fail to capture complex relationships of the studied drivers of behavior. Based on a survey of university students in Abu Dhabi (UAE, n = 519) and Aachen (GER, n = 153), this study investigates the impact of demographic characteristics, personality traits, energy-related beliefs, and social perceptions on the energy conservation motivation and attempts to save energy. Two distinct modeling approaches are applied and compared: (i) traditional linear regression (often used by social scientists), and (ii) machine learning based random forest regression (often used by data scientists). When using the training and testing set from the same location, the traditional linear regression showed slightly higher predictive power and significantly higher explanatory power. In contrast, the random forest regression was more successful in predicting the participants’ attitude towards energy saving by generalizing the behavior from different locations. In either case, the most influential drivers of occupant behavior were social influence and energy conservation beliefs and ability. The Abu Dhabi and Aachen samples shared the same drivers (i.e., triggers) of behavior despite disclosing different motivation levels and attempts to conserve energy. The findings shed meaningful insights on the multi-domain nature of occupant behavior across different cultures and the premise of different data analysis approaches to capture complex relationships." @default.
• W4220700805 created "2022-04-03" @default.
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• W4220700805 date "2022-08-01" @default.
• W4220700805 modified "2023-10-04" @default.
• W4220700805 title "Crossing borders and methods: Comparing individual and social influences on energy saving in the United Arab Emirates and Germany" @default.
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• W4220700805 doi "https://doi.org/10.1016/j.erss.2022.102561" @default.
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