Designing Online Virtual Advisors to Encourage Customer Self-disclosure: A Theoretical Model and an Empirical Test
Bibliographic record
Abstract
Virtual advisors (VA) are tools that assist users in making decisions. Using VAs necessitates the disclosure of personal information, especially when they are employed in personalized contexts such as healthcare, where disclosure is vital to providing valid and accurate advice. Yet, extant research has largely overlooked the factors that encourage or inhibit users’ from disclosing to VAs. In contrast, this study investigates the determinants of users’ intentions to self-disclose, and examines how VAs can be designed to enhance these intentions. The results of a study in the context of skin care advice reveal that the intention to disclose to a VA is not only the product of a rational process, but that perceptions of the VA and the relationship with it are important. The results further show that a parsimonious set of design elements can be used to endow a VA with desired characteristics that enhance the willingness to disclose. The study contributes to our understanding of the factors influencing users’ intentions to provide personal information to a VA, which extend beyond the expected benefits and costs. The study further demonstrates that social exchange theory can be applied in contexts in which humans are interacting with automated VAs.
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 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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".