ThermoCoach

Pisharoty, Devika; Yang, Rayoung; Newman, Mark; Whitehouse, Kamin

doi:10.1145/2821650.2821671

Cited by 25 publications

(3 citation statements)

References 11 publications

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“…Systems can behave with initiative without being presented as agents. Prior work has compared the user experience of systems with different degrees of initiative in a variety of domains [8,9,18]. While these systems act with various levels of agency, they are not presented in an anthropomorphic fashion.…”

Section: Abstractions Framework For Conceptual Modelsmentioning

confidence: 99%

Devices and Data and Agents, Oh My

Clark

Newman

Dutta

2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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With the advent of DIY smart homes and the Internet of Things comes the emergence of user interfaces for domestic humanbuilding interaction. However, the design trade-offs between the different representations of a smart home's capabilities are still not well-understood. In this work, we examine how four different smart home abstractions affect end users' mental models of a hypothetical system. We develop four questionnaires, each of which describes the same hypothetical smart home using a different abstraction, and then we collect responses depicting desired smart home applications from over 1,500 Mechanical Turk workers. We find that the choice of abstraction strongly primes end users' responses. In particular, the purely device-oriented abstraction results in the most limited scenarios, suggesting that if we want users to associate smart home technologies with valuable high-level applications we should shift the UI paradigm for the Internet of Things from device-oriented control to other abstractions that inspire a greater diversity of interactions. CCS Concepts: • Human-centered computing → User studies; Ubiquitous and mobile computing design and evaluation methods;

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Section: Abstractions Framework For Conceptual Modelsmentioning

confidence: 99%

Devices and Data and Agents, Oh My

Clark

Newman

Dutta

2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

Self Cite

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“…Yang & Newman (2012) found that a critical contributing factor leading to disuse of the Nest smart home thermostat was uncertainty about what the system was doing [19]. Pisharoty et al (2015) found that a smart thermostat design that learned user habits and then presented three possible schedules for a user to choose from reduced energy usage by an estimated 4.7% over manual programming and 12.4% over Nest (which does not present any options) [20]. However, this schedule was set only once at the beginning of the 3-month study and did not examine how users would respond to changes in the thermostat settings due to, for example, demand issues, current weather conditions, or changes in occupancy.…”

Section: Introductionmentioning

confidence: 99%

Don’t touch that dial: Psychological reactance, transparency, and user acceptance of smart thermostat setting changes

Heatherly,

Baker,

Canfield

2023

PLoS ONE

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Automation inherently removes a certain amount of user control. If perceived as a loss of freedom, users may experience psychological reactance, which is a motivational state that can lead a person to engage in behaviors to reassert their freedom. In an online experiment, participants set up and communicated with a hypothetical smart thermostat. Participants read notifications about a change in the thermostat’s setting. Phrasing of notifications was altered across three dimensions: strength of authoritative language, deviation of temperature change from preferences, and whether or not the reason for the change was transparent. Authoritative language, temperatures outside the user’s preferences, and lack of transparency induced significantly higher levels of reactance. However, when the system presented a temperature change outside of the user’s preferences, reactance was mitigated and user acceptance was higher if the thermostat’s operations were transparent. Providing justification may be less likely to induce psychological reactance and increase user acceptance. This supports efforts to use behavioral approaches, such as demand response, to increase sustainability and limit the impacts of climate change.

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“…Innovations in remote sensing and machine learning offer the chance to improve behavior-based demandside management strategies [40], including more effective eco-feedback interfaces that are salient, precise, and motivating [42] and "eco-feedforward" advice and prompts for personalized actions or new routines households could assimilate into their lifestyles [43]. Pilot studies around such programs have already begun to show initial promise [44][45][46].…”

Section: Introductionmentioning

confidence: 99%

Of impacts, agents, and functions: An interdisciplinary meta-review of smart home energy management systems research

McIlvennie

Sanguinetti

Pritoni

2020

Energy Research & Social Science

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Smart home energy management technologies (SHEMS) have long been viewed as a promising opportunity to manage the way households use energy. Research on this topic has emerged across a variety of disciplines, focusing on different pieces of the SHEMS puzzle without offering a holistic vision of how these technologies and their users will influence home energy use moving forward. This paper presents the results of a systematic, interdisciplinary meta-review of SHEMS literature, assessing the extent to which it discusses the role of various SHEMS components in driving energy benefits. Results reveal a bias towards technical perspectives and controls approaches that seek to drive energy impacts such as load management and energy savings through SHEMS without user or third-party participation. Not only are techno-centric approaches more common, there is also a lack of integration of these approaches with user-centric, information-based solutions for driving energy impacts. These results suggest future work should investigate more holistic solutions for optimal impacts on household energy use. We hope these results will provoke a broader discussion about how to advance research on SHEMS to capitalize on their potential contributions to demand-side management initiatives moving forward.

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ThermoCoach

Cited by 25 publications

References 11 publications

Devices and Data and Agents, Oh My

Devices and Data and Agents, Oh My

Don’t touch that dial: Psychological reactance, transparency, and user acceptance of smart thermostat setting changes

Of impacts, agents, and functions: An interdisciplinary meta-review of smart home energy management systems research

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