User Perceptions and Employment of Interface Agents for Email Notification
Bibliographic record
Abstract
This study investigates user perceptions and employment of interface agents for email notification to answer three research questions pertaining to user demographics, typical usage, and perceptions of this technology. A survey instrument was administered to 75 email interface agent users. Current email interface agent users are predominantly male, well-educated and well-off innovative individuals who are occupied in the IS/IT sector, utilize email heavily and reside in an English-speaking country. They use agents to announce incoming messages and calendar reminders. The key factors why they like to use agents are perceived usefulness, enjoyment, ease of use, attractiveness, social image, an agent’s reliability and personalization. The major factors why they dislike doing so are perceived intrusiveness of an agent, agent-system interference and incompatibility. Users envision ‘ideal email notification agents’ as highly intelligent applications delivering messages in a non-intrusive yet persistent manner. A model of agent acceptance and use is suggested. [Article copies are available for purchase from InfoSci-on-Demand.com]
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".