<b>Research Note</b>—The Influence of Trade-off Difficulty Caused by Preference Elicitation Methods on User Acceptance of Recommendation Agents Across Loss and Gain Conditions
Bibliographic record
Abstract
Prior studies on product recommendation agents (RAs) have been based on the effort-accuracy perspective in which the amount of effort required to make a decision and the accuracy of such decisions are two dominant antecedents of user acceptance of RAs. The current study extends the effort-accuracy perspective by considering trade-off difficulty, a type of negative emotion that arises when attainment of one's goals is blocked by the attainment of other goals; consequently, one must make trade-offs among the conflicting goals. Many product purchase choices for which RAs are used require users to make trade-offs among conflicting product attributes. A key feature of RAs, the preference elicitation method (PEM), often compels users to make explicit trade-offs. We examine whether an RA's PEM generates trade-off difficulty, which, in turn, affects users' evaluations (i.e., perceived amount of effort and perceived accuracy of recommendations) and the resultant acceptance of the RA. Trade-off difficulty influences users' evaluations of an RA via perceived control over execution of the RA PEM. In addition, the decision context in which users employ a PEM moderates the degree to which that PEM generates trade-off difficulty. Specifically, a PEM generates a greater degree of trade-off difficulty in a choice context that leads to a loss than in a choice context that leads to a gain. Consequently, users exert more effort to cope with trade-off difficulty in a loss condition. Because users voluntarily spend more effort, the negative influence of perceived effort on users' acceptance of an RA—which is supported in prior studies—decreases in a loss condition. A laboratory experiment was conducted using two between-subject factors: two RAs, one that employed a trade-off-compelling PEM and the other a trade-off-hiding PEM, and two decision contexts, one of which was a loss condition and the other a gain condition. The results supported all of the hypotheses.
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 | 0.009 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".