Examining the adoption and continuous usage of context-aware services
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.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".