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Reporting normative feedback to residential energy consumers has been found effective at reducing residential energy consumption. Upon receiving normative feedback households tend to modify their use to become in line with group norms. The effect of normative messages is partially moderated by how personally relevant normative reference groups are to the individual. Advanced energy metering technologies capture households’ energy use patterns, making it possible to generate highly similar and relevant normative reference groups in a non-invasive manner. Unfortunately, it is not well understood how similar individuals are to other group members. It also remains unknown how much individuals identify with behavioral reference groups. Therefore, this research aims to investigate how households perceive behavioral reference groups used in normative comparisons. Survey questionnaires are collected from 2,008 participants using Amazon Mechanical Turk. It is found that while households’ behaviors are more similar when grouped based on energy use profiles than based on geographic proximity, they identify more closely with proximity-based groups. Also, members’ group identification increases as individuals have higher similarity in energy use behaviors with other group members. This implies that enhancing the identity of profile-based behavioral reference groups will lead to an increase in norm adherence, and in turn reductions in household energy use. 

Psychologists hypothesize that the effectiveness of normative messaging interventions increases when individuals have more personal attachment and similarity with reference groups. Using readily available energy consumption data, it is now possible to create highly personalized reference groups based on households’ daily energy use in a non-invasive matter. However, it still remains unclear to what degree individuals perceive behavioral reference groups as a cohesive entity. Therefore, this research investigates how individuals perceive energy profile-based groups relative to more standard geographic proximity-based groups. An online survey is conducted with 1,928 U.S. adults. Individuals do not perceive the profile-based groups as very entitative groups. Also, similarity between energy profile-based group members indirectly affects individuals’ identification with the groups via group entitativity. Lastly, this indirect effect is larger than the direct effect of similarity between group members on group identification. These results imply that a better understanding of what affects group entitativity would allow interveners to create more effective normative feedback messages. 

Normative messaging interventions have proven to be a cost-effective strategy for promoting pro-environmental behaviors. The effectiveness of normative messages is partially determined by how personally relevant the comparison groups are as well as the lag of feedback. Using readily available energy use data has created opportunities to generate highly personalized reference groups based on households’ behavioral patterns. Unfortunately, it is not well understood how data granularity (e.g., minute, hour) affects the performance of behavioral reference group categorization. This is important because different levels of data granularity can produce conflicting results in terms of group similarity and vary in computational time. Therefore, this research aims to evaluate the performance of clustering methods across different levels of temporal granularity of energy use data. A clustering analysis is conducted using one-year of energy use data from 3,000 households in Holland, Michigan. The clustering results show that behavioral reference groups become the most similar when representing households’ energy use behaviors at a six-hour interval. Computationally, less granular data (i.e., six and twelve hours) takes less time than highly granular data which increases exponentially with more households. Considering the enormous scale that normative messaging interventions need to be applied at, using less granular data (six-hour intervals) will permit interveners to maximize the effectiveness of highly personalized normative feedback messages while minimizing computation burdens. 

Abstract The interconversion of charge and spin currents via spin-Hall effect is essential for spintronics. Energy-efficient and deterministic switching of magnetization can be achieved when spin polarizations of these spin currents are collinear with the magnetization. However, symmetry conditions generally restrict spin polarizations to be orthogonal to both the charge and spin flows. Spin polarizations can deviate from such direction in nonmagnetic materials only when the crystalline symmetry is reduced. Here, we show control of the spin polarization direction by using a non-collinear antiferromagnet Mn 3 GaN, in which the triangular spin structure creates a low magnetic symmetry while maintaining a high crystalline symmetry. We demonstrate that epitaxial Mn 3 GaN/permalloy heterostructures can generate unconventional spin-orbit torques at room temperature corresponding to out-of-plane and Dresselhaus-like spin polarizations which are forbidden in any sample with two-fold rotational symmetry. Our results demonstrate an approach based on spin-structure design for controlling spin-orbit torque, enabling high-efficient antiferromagnetic spintronics. 

The metal-insulator transition in correlated materials is usually coupled to a symmetry-lowering structural phase transition. This coupling not only complicates the understanding of the basic mechanism of this phenomenon but also limits the speed and endurance of prospective electronic devices. We demonstrate an isostructural, purely electronically driven metal-insulator transition in epitaxial heterostructures of an archetypal correlated material, vanadium dioxide. A combination of thin-film synthesis, structural and electrical characterizations, and theoretical modeling reveals that an interface interaction suppresses the electronic correlations without changing the crystal structure in this otherwise correlated insulator. This interaction stabilizes a nonequilibrium metallic phase and leads to an isostructural metal-insulator transition. This discovery will provide insights into phase transitions of correlated materials and may aid the design of device functionalities.