Bibliographic record
Abstract
Mobile access has opened new vistas for various sectors of society including businesses. The ability that anyone using (virtually) any device could be reached anytime and anywhere presents a tremendous commercial potential. Indeed, the number of mobile applications has seen a tremendous growth in the last few years. In retrospect, the fact that almost anyone can set up a mobile application claiming to offer products and services raises the question of credibility from a consumer’s viewpoint. The obligation of establishing credibility is essential for an organization’s reputation (Gibson, 2002) and for building consumers’ trust (Kamthan, 1999). If not addressed, there is a potential for lost consumer confidence, thus significantly reducing the advantages and opportunities the mobile Web as a medium offers. If a mobile application is not seen as credible, we face the inevitable consequence of a product, however functionally superior it might be, rendered socially isolated. The rest of the article is organized as follows. We first provide the motivational background necessary for later discussion. This is followed by introduction of a framework within which different types of credibility in the context of mobile applications can be systematically addressed and thereby improved. Next, challenges and directions for future research are outlined. Finally, concluding remarks are given.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".