A Process Tracing Study on Trust Formation in Recommendation Agents
Bibliographic record
Abstract
This study utilizes a processing tracing method to explore the processes of trust formation in web-based productbrokering recommendation agents (RAs). We compare and contrast the processes of trust/distrust formation in an attribute-based RA (a typical content-based RA) versus a need-based RA (a content-based RA plus need-based questions). Concurrent verbal protocols from 49 subjects were collected, transcribed, and analyzed. Our protocol analysis results show that the need-based RA elicits significantly more trust formation processes and fewer distrust formation processes than the attribute-based RA does, which explains why the level of customer trust in the need-based RA is significantly higher than the level of customer trust in the attribute-based RA. Interestingly, our results show that, for both types of RAs, the top three processes of trust formations are different from the top three processes of distrust formations. Suggestions are given on how to design more trustworthy RAs.
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 | 0.007 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 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".