<b>Research Note</b>—A Contingency Approach to Investigating the Effects of User-System Interaction Modes of Online Decision Aids
Bibliographic record
Abstract
Interactive online decision aids often employ user-decision aid dialogues as forms of user-system interaction to help construct and elicit users' attribute preferences about a product type. This study extends prior research on online decision aids by investigating the effects of a decision aid's user-system interaction mode (USIM), which can be either user-guided or system-controlled, on users' effort-related (number of iterations of using the aid and perceived cognitive effort expended in using it) and quality-related (perceived quality of the aid and acceptance of the product advice it provides) assessments. A contingency approach with two moderating factors is employed. One factor is the decision strategy (additive-compensatory or elimination) employed by the aid, and the other is the users' product knowledge (high or low). A laboratory experiment was conducted to compare online decision aids with different USIMs. Although the results largely confirm that users assess the user-guided USIM more positively than the system-controlled USIM, the effects of USIM are stronger in two settings: for the elimination-based aid than for the additive-compensatory-based aid and for users with low product knowledge than for those with high product knowledge, especially in terms of effort assessments. This research advances the theoretical understanding of the effects of interaction between two critical components of online decision aids (USIMs and decision strategies) and the moderating role of user characteristics (product knowledge) in affecting users' evaluations. It also provides practitioners with design advice for developing these aids.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".