Occupants’ willingness to share information for improved comfort and energy efficiency in offices
Bibliographic record
Abstract
• Influences on occupants’ willingness to share information are investigated • 791 samples were collected with a stated preference discrete choice experiment • Sharing demographic and physical environmental data is widely acceptable • Heightened concerns exist about sharing psychological and activity-related data • Anonymity and control over the data appear to be of crucial importance Human environmental perception and occupant behaviour are influenced by a multitude of factors, including demographic variables and individual preferences. Advancements in data collection allow the acquisition of extensive personal information, such as heart rate, skin temperature, and emotional responses to environmental conditions. These data can enhance research on multi-domain influences and on optimizing building operations but raise questions regarding individuals' willingness to share personal information. This study investigates how factors like data type, data collector, and anonymity level are associated with occupants’ willingness to share information for improved indoor environmental conditions or energy efficiency. A stated preference discrete choice experiment was developed and applied, with responses collected from participants in 29 countries, resulting in a dataset with 791 samples. The discrete choice analysis was conducted using mixed logit models and based on Random Utility Theory. The outcomes indicate that respondents exhibit relative indifference toward sharing demographic and physical environmental data, while having heightened concerns about sharing psychological and activity-related information. Anonymity and control over the data appear to be of crucial importance. Additionally, data collection by academic institutions is preferred to that by for-profit entities. Variability in willingness to share data across and within samples of countries suggests a necessity for tailored strategies. This research underscores the necessity of balancing advancements in energy efficiency and thermal comfort with societal needs that respect individual rights. Practical recommendations for effective personal data collection are provided and methodological limitations due to scenario complexity and participant engagement are highlighted.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".