The impact of distractions on the usability and the adoption of mobile devices for wireless data services
Bibliographic record
Abstract
Mobile devices are becoming increasingly popular, having already reached over 1.5 billion mobile subscribers.Although progress has been made in terms of technological innovations, usability challenges still face m-Business (mobile business) application.This paper explores how the context of use impacts the usability of mobile devices.An empirical study was undertaken to investigate the impact of distractions on the usability and its subsequent effect on consumers' behavioural intention towards using a Personal Digital Assistant (PDA) for wireless data services.Distractions were simulated in this study in the form of either user motion or environmental noise (i.e.background auditory and visual stimuli).A structural equation modelling analysis confirmed the impacts of distractions on perceived usability (i.e.efficiency and effectiveness) of, and in turn the users' satisfaction with and behavioural intention to use, a PDA for wireless data services.Implications of these findings for theory, practice, and future research are outlined.
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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 | 0.025 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".