This paper presents a field study aiming to understand and improve the energy and thermal comfort performance of air-source heat pumps (ASHPs) in cold climates. The study was conducted in a residential community of 22 single-family homes in Indianapolis, Indiana, equipped with smart thermostats, circuit-level power meters, and indoor temperature sensors. Field measurements and resident interviews were first used to characterize thermostat adjustments and their implications for heating-related electricity use. Baseline data revealed that heating-related electricity use varied significantly, by up to a factor of two, across physically similar homes, indicating that ASHP field performance was shaped not only by weather, equipment, and envelope characteristics, but also by how residents operated their thermostats and supplemental heating devices. A Smart Energy Assistant was then developed for the first time, to detect energy-inefficient thermostat adjustments and implement targeted control actions, while communicating each action and allowing residents to cancel or override it. These control actions were designed to reduce unnecessary auxiliary heat use, avoid inefficient recovery loads, improve temperature distribution, and encourage more efficient use of the central heat pump system. During the two-month deployment, the Smart Energy Assistant generated 1,643 actions across 22 homes, with 88% overall acceptance rate, but with uneven distribution across homes. Overall, the findings indicate that improving ASHP performance in occupied homes requires both better understanding of resident thermostat adjustments and equipment control strategies as well as transparent communication and consideration of resident thermal comfort and agency.

Existing research models EV charging choice as tradeoffs among station attributes, obscuring qualitatively distinct behavioral profiles that govern how tradeoffs are formed. Using survey data from 365 BEV owners, we combine factor analysis, latent profile analysis, and inverse propensity weighting (IPW) to identify preference dimensions, segment users, and estimate causal drivers of profile membership. Factor analysis reveals eight latent dimensions spanning station-facing choice attributes and user-facing contextual factors. Latent profile analysis identifies two groups: Efficiency-Oriented Users (76%), prioritizing accessibility and cost, and Information-Responsive Users (24%), emphasizing provider trust, amenities, and situational compatibility. IPW estimates show that private charger access (OR = 2.39), business use (OR = 2.70), and public charging frequency (weekly OR = 7.43) increase Information-Responsive membership, while commuting use decreases it (OR = 0.47). These results demonstrate that preference heterogeneity is shaped by infrastructural access and behavioral routines, offering a preference-based explanation for station underutilization and underscoring the need for differentiated infrastructure planning.

Effects of sensory experiences on social influence processes in decision-making groups are explored. In two experiments, members of three-person groups selected auditory samples such as background music for a hotel. While all members had access to written descriptions of the auditory samples, one designated member could also listen to the auditory samples (member with sensory experience). Experienced members exerted more influence on group decisions than inexperienced members. Exploratory analyses revealed that experienced members’ narratives were perceived as more credible, but not more vivid than those of inexperienced members. A linguistic analysis of group discussions showed that members with sensory experience used more auditory words and metaphors than inexperienced members. Future directions to study the role of sensory experience in group decision making are discussed.

Adopting renewable energies at the individual level is required to mitigate greenhouse gas emissions, reduce reliance on fossil fuels, and transition to a society based on green energy. This study taps into this topic by exploring why and which consumers would adopt renewable energies using two psychological approaches (i.e., protected values and the halo effect) and by looking at the role of consumers’ demographics in their willingness to pay for renewable energy. Evidence of a comfort halo effect was found for consumers with high protected values (i.e., with a moral orientation toward the environment) using two-instance repeated-measures linear regressions in an MTurk experiment: Those consumers are likely to perceive more comfort at home when they know their electricity is sourced from renewable energies. This effect does not hold for consumers who have low protected values. Consumers with high protected values were not willing to pay more for renewable energies, though. Conversely, consumers with low protected values were willing to pay a premium for non-renewable energy. Overall, consumers with high education expressed a willingness to pay more for renewable energies, but consumers with high income and younger consumers did not. This research highlights the importance of shifting from fossil fuels to a low-carbon future by stressing the role of the factors that may affect consumer decision making.

In marketing efforts, messages are commonly tailored to audiences to ensure effective outcomes. One way to tailor messages consists in providing arguments that appeal to the characteristics of a specific target audience. However, this strategy can decrease the message's appeal to a non-target audience. Drawing on the concept of strategic ambiguity, an alternative strategy to tailor messages is proposed and tested. Strategically ambiguous messages provide arguments that appeal to several audiences (multiple audience tailoring). These messages combine attributes that are relevant to distinct audiences, and audiences are expected to pay selective attention to attributes that are most relevant for them. The effectiveness of one-audience and multiple-audience tailoring was compared in an experiment with credit-card holders. Single audience tailoring was the most effective form of tailoring. Multiple audience tailoring was more effective than unmatched tailoring in various conditions. The study enhances our understanding of message tailoring using ambiguity to increase marketing effectiveness.

Digital assistants such as Alexa can provide feedback to residents that affect energy consumption. One important characteristic of feedback refers to the emotionality of the provided feedback. Research on social cognition and attribution theory suggests that effects of emotional messages on behavior are contingent on the inferred cause of the emotion (e.g., why a message was said in a happy or neutral voice). As a prerequisite, to have the intended effects on energy saving behaviors, Alexa’s emotional messages have to trigger three basic social cognitions: (1) the emotional display has to be identified by residents; (2) residents have to correctly identify their behavior as a target of the emotional display; and (3) residents have to attribute the emotional display to that behavior. In two studies (N = 194 and N = 353), several conditions were identified that triggered these three basic social cognitions in a simulated environment.

Characteristics of scales, such as the labels that are used on scales, have been shown to affect judgments. The scale-dependency hypothesis predicts specific effects of the properties of a temperature scale on residents’ choices of temperature setpoints. Based on the literature on anchoring in judgment and decision making, we assessed the effects of the displayed current temperature, midpoint, range, and increment of temperature scales on the selection of setpoint temperatures for residential homes. Participants (N = 384) were asked to imagine that they work as a manager of a residential apartment complex and to select, in this function, setpoint temperatures for incoming residents. The experiment revealed independent effects of the current temperature as well as the midpoint and range of the used scale on the selected setpoints. The scale increment did not systematically affect the chosen temperatures.

Terrorist threats and attacks provide major risks and sources of public crises in the 21st century. New probabilistic computing technologies possess the capability of increasing the success of identifying terrorist threats and solving cybersecurity and encryption problems more efficiently. However, to identify terrorist threats, these technologies would require the use of a large amount of personal data, which cause potential concerns for privacy. We offer a social-identity explanation of public support for the detection of terrorist threats through the use of online personal data. A survey study ( N = 1,204) revealed strong support for the provided social-identity explanation of public perceptions. As expected, respondents displayed in-group favoritism by more strongly supporting the use of private personal information from out-group members (non-U.S. citizens) than from in-group members (U.S. citizens). The observed in-group favoritism was most pronounced when respondents had both a strong national identity and a strong sense of general privacy concern. The observed differences were independent of respondents’ political orientation and age.

Solar panels promise to provide clean energy with much fewer environmental impacts than traditional energy sources. The adoption of solar panels may be practiced on an individual level, a community level, or even at a national level through corporate or government actions. While extant literature has observed that perceptions of monetary value, house features, and environmental benefit predict the use of solar panels, less is known about how social factors relate to the use of solar panels. The Social Identity Model of Pro-Environmental Action (SIMPEA; Fritsche et al. in Psychol Rev 125(2):245–269, 2018 ) hypothesizes that ingroup identification, along with collective efficacy and perceived norms of the ingroup, predict pro-environmental behavior. We applied this prediction to solar panels as a new context for SIMPEA and tested this hypothesis with a survey measuring participants’ support for the adoption of solar panels in community and national contexts. The study revealed a predicted two-way interaction between identification and norms, but there was no evidence of the three-way interaction that was predicted by SIMPEA.

An increasing number of residential homes are equipped with smart assistants such as Cortana, Alexa, and Siri. Adoption rates and the frequency of the usage of smart assistants vary across users and residential homes. Building on the theory of uses and gratifications (UGT) and the unified theory of acceptance and use of technology 2 (UTAUT2), the objective of this paper was to examine whether the intended use of a digital assistant would moderate the effects of performance expectancy and hedonic motivation on its adoption. Two experiments (N = 345 and N = 351) tested the hypothesis that, for utilitarian purposes, devices with high performance appraisal are preferred, whereas for entertainment purposes, devices with high hedonic appraisal are preferred. The experiments manipulated the performance expectancy and hedonic motivation towards several digital assistants by varying how the assistants were introduced. Participants were asked which assistant they would choose for a variety of utilitarian and entertainment purposes. As expected, the experiments supported the proposed matching hypothesis, revealing that the devices that were high in performance appraisal were preferred for utilitarian tasks, whereas the devices high in hedonic appraisal were preferred for entertainment needs. These results suggest that a device’s introduction can change people’s perceptions of the device and subsequently their decision to use it.

Advancements in big data analytics offer new avenues for the analysis and deciphering of suspicious activities on the internet. One promising new technology to increase the identification of terrorism threats is based on probabilistic computing. The technology promises to provide more efficient problem solutions in encryption and cybersecurity. Probabilistic computing technologies use large amounts of data, though, which raises potential privacy concerns. A study ( N = 1,023) was conducted to survey public support for using probabilistic computing technologies to increase counterterrorism efforts. Overall, strong support was found for the use of publicly available personal information (e.g., personal websites). Regarding private personal information (e.g., online conversations), respondents perceived it to be more appropriate to use information from out-group members (non-American citizens) than from in-group members (American citizens). In line with a social-identity account, this form of in-group favoritism was strongest among respondents displaying a combination of strong national identities and strong privacy concerns.

In this paper, we present a first-time cloud-based eco-feedback and gaming platform that aims to promote energy conserving thermostat-adjustment behaviors in multi-unit residential buildings. To achieve this goal, we introduce a new modeling approach for personalized eco-feedback design integrated with a collaborative social game to assist residents enhance their thermostat use while promoting community-level energy savings. Our modeling framework is integrated into a cloud-based application, MySmartE, with visual (wall-mounted tablet) and voice (Alexa) user interfaces to facilitate behavioral changes in a user-centric approach. The platform is deployed in a multi-unit residential community in Fort Wayne, IN, and data from the field study are used to investigate: (i) how occupants' thermostat behaviors changed after using the MySmartE app; (ii) how users interacted with the app during the game; and (iii) how was users' experience with the developed platform. Despite the heterogeneous characteristics of households, the results from the field study showed the positive effect of the intervention in the thermostat-adjustment behaviors, which resulted in an increase in the indoor temperature during the cooling season compared to the baseline. Findings from the user interaction analysis and post-experiment interviews also revealed the significant potential to nudge households’ energy conservation behaviors with the developed platform along with the challenges that should be tackled to derive long-term behavior changes.

Drawing from research on the halo effect and protected values, consumers’ adoption intentions and willingness to pay a premium for renewable energy were explored. Two theoretical models that involve moderated mediation were tested through two-instance repeated-measures linear regressions and non-parametric tests in a behavioral experiment with an Amazon MTurk sample. In line with the expected halo effect, the effects of the renewability of the energy sources on consumers’ adoption intentions and willingness to pay a premium were mediated through consumers’ perceived comfort. These mediation effects were stronger among consumers with high protected values compared to those with low protected values. The results suggest that the positive evaluations of renewable energies by consumers with high protected values are mainly driven by those values. Conversely, consumers with low protected values would have lower adoption intentions, would be less willing to pay more, and they would not feel comfort at home when using renewable energy compared to consumers with high protected values.

Widespread efforts are being made to mitigate environmental degradation driven by human activities. From a supply chain management perspective, companies aim at improving their environmental and organizational performance along their supply chain simultaneously. Since consumers are the sources of manufacturing companies' profitability, companies are interested in understanding the extent to which consumers care about their green practices. However, while some consumers would have a higher willingness to pay a premium (WPP) or purchase intention (PI) for environmentally differentiated products, others would not. Moreover, there is scant evidence regarding the integrated effects of intra- and inter-organizational green supply chain practices on green consumerism. Therefore, this study adopts two psychological approaches (i.e., protected values and halo effect) to describe this relationship based on two models that encompass mediation and moderation effects. Data were collected from 351 Colombian university students through a behavioral experiment with three product-based conditions, and the hypotheses were tested using two-instance repeated-measures linear regressions and non-parametric tests. The results indicate that perceived product performance mediates the effect of green supply chain practices on consumers' WPP and PI (halo effect). Additionally, consumers' moral orientation toward the environment (protected values) moderates the effects of green supply chain practices on consumers' WPP, PI and perceived product performance. The study found that people who hold protected values evaluate products better not just for its green attributes, but because of their increased perception of the products' performance. The contributions are centered on the role of psychological approaches in green supply chain studies to understand consumers' preferences.