Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Evolving interaction: a qualitative investigation of user mental models for smart thermostat users

Evolving interaction: a qualitative investigation of user mental models for smart thermostat users Smart thermostats differ significantly from traditional devices and are quickly becoming commonplace in homes. Literature demonstrates that thermostat interfaces greatly influence user interaction and related energy outcomes. Moreover, how users imagine their device to work appears to have a greater impact on usage than how the system functions. Previous work investigated manual and programmable thermostats in this context, employing user mental models (UMMs) to analyse user understanding. Since then, thermostats have developed significantly. This paper presents a novel investigation of smart thermostat UMMs. It employs contemporary methods to construct ten UMM diagrams, and three detailed case studies, contextualized with previous findings. All participants demonstrated feedback theory. Case studies highlight common misconceptions. Overall, smart thermostat UMMs appear to enable effective usage; however, some users are overwhelmed by the complexity, limiting engagement and use of features (e.g. programming). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Architectural Science Review Taylor & Francis

Evolving interaction: a qualitative investigation of user mental models for smart thermostat users

Download PDF
17 pages

Loading next page...
 
/lp/taylor-francis/evolving-interaction-a-qualitative-investigation-of-user-mental-models-9b2MEK9XAI

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Taylor & Francis
Copyright
© 2023 Informa UK Limited, trading as Taylor & Francis Group
ISSN
1758-9622
eISSN
0003-8628
DOI
10.1080/00038628.2023.2201253
Publisher site
See Article on Publisher Site

Abstract

Smart thermostats differ significantly from traditional devices and are quickly becoming commonplace in homes. Literature demonstrates that thermostat interfaces greatly influence user interaction and related energy outcomes. Moreover, how users imagine their device to work appears to have a greater impact on usage than how the system functions. Previous work investigated manual and programmable thermostats in this context, employing user mental models (UMMs) to analyse user understanding. Since then, thermostats have developed significantly. This paper presents a novel investigation of smart thermostat UMMs. It employs contemporary methods to construct ten UMM diagrams, and three detailed case studies, contextualized with previous findings. All participants demonstrated feedback theory. Case studies highlight common misconceptions. Overall, smart thermostat UMMs appear to enable effective usage; however, some users are overwhelmed by the complexity, limiting engagement and use of features (e.g. programming).

Journal

Architectural Science Review – Taylor & Francis

Published: May 4, 2023

Keywords: User mental model; smart thermostat; human-computer interaction; usability; qualitative methods; feedback theory

References

  • A Method for Fully Automatic Operation of Domestic Heating
  • Controls for End Users: A Guide for Good Design and Implementation
  • Using Knowledge Structures to Redesign an Instructor–Operator Station
  • An Investigation into Usability and Exclusivity Issues of Digital Programmable Thermostats
  • Living in Cold Homes After Heating Improvements: Evidence from Warm-Front, England’s Home Energy Efficiency Scheme
  • Understanding High Performance Buildings: The Link Between Occupant Knowledge of Passive Design Systems, Corresponding Behaviors, Occupant Comfort and Environmental Satisfaction
  • Comfort and Control: Energy and Housing for the Elderly
  • The Evaluation of an Innovative Intelligent Thermostat Interface: Universal Usability and Age Differences
  • Japanese Residential Air-Conditioning: Natural Cooling and Intelligent Systems
  • Exploring Smart Thermostat Users’ Schedule Override Behaviors and the Energy Consequences
  • User Problems with Individual Temperature Control in Offices
  • Two Theories of Home Heat Control
  • Green Expectations: The Story of a Customizable Lighting Control Panel Designed to Reduce Energy Use
  • Efficient and Inefficient Aspects of Residential Energy Behaviour: What Are the Policy Instruments for Change?
  • The Design with Intent Method: A Design Tool for Influencing User Behaviour
  • The Importance of Usability in Product Choice: A Mobile Phone Case Study
  • Principles from the Psychology of Thinking and Mental Models
  • Usability of Residential Thermostats: Preliminary Investigations
  • Thermostat Strategies Impact on Energy Consumption in Residential Buildings
  • Facilitating Energy Savings with Programmable Thermostats: Evaluation and Guidelines for the Thermostat User Interface
  • How People Use Thermostats in Homes: A Review
  • Energy Efficiency and the Misuse of Programmable Thermostats: The Effectiveness of Crowdsourcing for Understanding Household Behavior
  • The Famous Five Factors in Teamwork: A Case Study of Fratricide
  • Case Studies of Mental Models in Home Heat Control: Searching for Feedback, Valve, Timer and Switch Theories
  • Mind the Gap–Deriving a Compatible User Mental Model of the Home Heating System to Encourage Sustainable Behaviour
  • The Quick Association Check (QuACk): A Resource-Light, ‘Bias Robust’ Method for Exploring the Relationship Between Mental Models and Behaviour Patterns with Home Heating Systems
  • When Energy Saving Advice Leads to More, Rather Than Less, Consumption
  • Mental Model Interface Design: Putting Users in Control of Home Heating
  • Designing for the Home: A Comparative Study of Support Aids for Central Heating Systems
  • Central Heating Thermostat Settings and Timing: Building Demographics
  • Residential Thermostat Usability: Comparing Manual, Programmable, and Smart Devices
  • Residential HVAC Runtime from Smart Thermostats: Characterization, Comparison, and Impacts
  • A Behavioural Model of Temperature Controller Usage and Energy Saving
  • Refining the Test Phase of Usability Evaluation: How Many Subjects Is Enough?