Examining the adoption and continuous usage of context-aware services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Context-aware services such as Global Positioning System (GPS) and Location Based Services (LBS) can be used to acquire information and services at any time from anywhere in various contexts. It is critical to study how user perceptions and intentions are affected in different decision-making processes. Based on the Technology Acceptance Model and Expectation Confirmation Theory, this research examines a two-stage theoretical model of consumer adoption of context-aware services by studying an example of an intelligent tourist guide Xi-Hu-Tong (West Lake tour). We focus on the formation mechanisms of user decisions in the initial adoption stage, and on feedback and evaluation mechanisms in the post-adoption stage. According to our data analysis using structural equation modeling, we find that relative advantage, motivational needs, and personal situations have significant impacts on user initial adoption intention. Additionally, usage experience has a significant impact on expectation confirmation and satisfaction. Usage experience also influences user satisfaction, reinforcing the emergence of post-adoption behaviors such as continuous usage and recommendations. Together, these results illustrate the dynamic process that encourages consumers who begin as potential users to eventually become loyal users.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it