Smart Home User Perception in Canada: National Cross-Sectional Survey (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Smart Home Technology (SHT) encompasses Internet-connected interfaces, sensors, monitors, devices, and appliances, which are networked together to allow for automation as well as control of the home environment. They can facilitate tasks such as taking medication or send emergency fall alerts. However, their widespread use comes with concerns of power imbalances between users, technology companies, marketers, state actors, and others regarding data collection, its use and disclosure, as well as security issues such as the potential for data breaches. Despite this, little is known about how Canadians perceive and understand their SHTs. Given Canada’s unique demographic diversity and distinct context, examining Canadian perspectives is necessary to advancing this body of research. </sec> <sec> <title>OBJECTIVE</title> This paper explores user perceptions of SHTs in Canada with a focus on four themes: privacy, purpose of data collection, risks and benefits, and safety. </sec> <sec> <title>METHODS</title> An online cross-sectional survey was conducted between March 7 – April 24, 2023 across Canada to collect self-reported demographic information and perceptions around the four aforementioned themes through multiple-choice and optional short response questions. The quantitative data was exported into SPSS and Python programming language for further analysis. </sec> <sec> <title>RESULTS</title> Survey data from a total of 881 SHT users was analysed. The presence of privacy cynicism was displayed via user mistrust (294/881, 33%), uncertainty (281/881, 32%), powerlessness (325/881, 37%), and digital resignation (232/881, 26%) as self-reported by users. Many users displayed a willingness to trade privacy for perceived benefits, such as convenience (503/881, 57%). Users also flagged enhanced safety and daily convenience as a beneficial feature of SHTs (492/881, 56%). Contrary to previous Internet or smart home research, most (801/881, 91%) participants reported having read their SHT Terms of Use documents upon setup. </sec> <sec> <title>CONCLUSIONS</title> This study illustrates themes of privacy cynicism and digital resignation within Canadian users, which are prevalent within an emerging body of related literature on Internet platforms more generally, and highlights ways in which to mitigate these patterns. The gap between user privacy preferences and options underscores the need for stronger user-centric design and data protection regulation. These insights and suggestions provide valuable guidance for policymakers and industry stakeholders navigating the complex landscape of SHT adoption. </sec>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".