A comprehensive acceptance model for smart home services
Bibliographic record
Abstract
Smart home services (SHSs) afford users an effective lifestyle management system, which provides human-oriented networking of smart devices and applications that enable users to control their homes from anywhere at any time. Despite the benefits of SHSs, however, their acceptance is very low. There remains a gap in the literature in terms of a comprehensive model that addresses users’ intention to use SHSs. To address this gap, the present study explored the factors that influence SHS acceptance among users based on well-established theoretical frameworks, such as the technology acceptance model, innovation diffusion theory, and the theory of planned behavior. To this end, the study integrated four additional factors, namely, perceived convenience, perceived connectedness, perceived cost, and perceived privacy risk, into the exploration and carried out structural equation modeling to quantitatively determine the effects of these factors. Questionnaires were administered to 750 users. The findings indicated that perceived compatibility, perceived convenience, perceived connectedness, perceived cost, perceived behavioral control with perceived usefulness, and perceived ease of use directly and indirectly exerted a significant influence on users’ intention to use SHSs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.004 | 0.001 |
| 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".