The Nature and Consequences of Trade-Off Transparency in the Context of Recommendation Agents1
Bibliographic record
Abstract
That recommendation agents (RAs) can substantially improve consumers’ decision making is well understood. Far less understood is the influence of specific design attributes of the RA interface on decision making and other outcome measures. We investigate a novel design for an RA interface that enables it to interactively demonstrate trade-offs among product attribute values (i.e., trade-off transparency feature) to improve consumers’ perceived product diagnosticity and perceived enjoyment. We also examine the extent to which the trade-offs among product attribute values should be revealed to the user. Further, based on the stimulus– organism–response model, we develop a theoretical model that extends the effort–accuracy framework by proposing perceived enjoyment and perceived product diagnosticity as two antecedents for perceived decision quality and perceived decision effort, respectively. In an experimental study, we find that (1) the trade-off transparency feature significantly affects perceived enjoyment and perceived product diagnosticity, (2) perceived enjoyment and perceived product diagnosticity follow an inverted U-shaped curve as the level of trade-off transparency increases, (3) although users spend more time understanding attribute trade-offs with the trade-off transparency feature, they are more efficient in selecting a product, (4) perceived enjoyment simultaneously leads to better perceived decision quality and lower perceived decision effort, and (5) perceived product diagnosticity leads to better perceived decision quality without compromising perceptions of decision effort. Theoretically, this study increases our understanding of how the design of an RA interface can improve consumers’ product diagnosticity and enjoyment, and proposes two antecedents to improve perceived decision quality and reduce perceived decision effort. For design practitioners, our results indicate the importance of providing the trade-off transparency design feature to potential consumers.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".