The Effects of Process and Outcome Similarity on Users' Evaluations of Decision Aids*
Bibliographic record
Abstract
ABSTRACT Decision aids (DA) used in online shopping contexts have been shown to improve users' product choices. Given that previous research (e.g., Byrne & Griffitt, 1973 ) has demonstrated the positive effects of perceived similarity on an individual's evaluation of others, this study investigates the effects of users' perceived similarity with a DA on their evaluations of that DA. More specifically, we investigate the effect of users' perceptions of the similarity between their own decision process and that followed by the DA to arrive at a recommendation (decision process similarity), as well as the similarity between the recommendations made by the DA and users' initial choices (outcome similarity), on their evaluations of the DA's usefulness and trustworthiness. The results of this study show that perceived process similarity exerts positive and significant effects on users' perceptions of the DA's usefulness and trustworthiness. However, the effects of perceived outcome similarity on trust are completely mediated by perceived process similarity. It is also observed that the level of the user's domain knowledge moderates the effects of perceived decision process similarity on both perceived usefulness and trustworthiness. These results have implications for DA design. It is important that designers consider the process by which users make decisions for themselves and align the DA's decision process with those of the user's, especially for the novice user. The full mediation of the effect of outcome similarity on trust by process similarity highlights how a similar decision process can mitigate some of the negative effects of outcome dissimilarity.
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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 | 0.006 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".