Perceived Risk and Fashion on the Intention to Adopt Wireless Earbuds in the United States Using a Partial Least Squares-Structural Equation Modeling Approach: Empirical Study
Bibliographic record
Abstract
Background: The number of studies on the use of smart wearables has increased dramatically in recent years. However, aspects including personal safety and fashion perspectives of wearable devices have not yet been adequately addressed in the literature. There have been debates regarding the potential health risks and fashionability of using wearable devices. Regardless of the actual impact of such devices, these aspects may influence users' perceptions toward the purchase and use of wearable technology. Objective: This paper addresses the following research question: How do perceptions of risk and fashion affect the user's intention to purchase and use wireless earbuds? Methods: A survey was administered to assess perceptions on health and privacy risks, fashionability, and wearable comfort of wireless earbuds, alongside questions on behavioral intention regarding their purchase and use. All questions were adapted from prior research and measured using a 7-point Likert scale. The final sample of 205 responses was analyzed using the partial least squares method with Smart-PLS software. Results: Perceived health risk (P=.015), perceived fashionability (P<.001), and wearable comfort (P=.007) had a significant impact on a consumer's intention to purchase wireless earbuds. Privacy risk did not have a significant impact on intention to purchase. Intention to purchase had a significant impact on intention to use (P<.001). Conclusions: As new types of emerging technology are introduced to the market, technology acceptance models should evolve to better understand consumers' perceptions toward these new technologies, from both academic and practical points of view.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".