Interactive Decision Aids for Consumer Decision Making in E-Commerce: The Influence of Perceived Strategy Restrictiveness1
Why this work is in the frame
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Bibliographic record
Abstract
This paper extends the effort–accuracy framework of cognition by taking into account the perceived strategy restrictiveness of decision aids, and tests the extended framework in a context in which online decision aids are used to elicit consumers’ preferences, automate the processing of the preferences, and provide product advice for consumers. Three types of decision aids with different decision strategy support capabilities (an additive-compensatory based aid, an elimination-based aid, and a hybrid aid supporting both strategies) are compared in terms of users’ perceptions of strategy restrictiveness, advice quality, and cognitive effort. These comparisons are grounded on the properties of normativeness and complementarity of decision strategies employed by the aids. A normative strategy takes into account both the users’ attribute preferences and the relative importance of such preferences, and allows for trade-offs among preferences (e.g., additive–compensatory). Strategy complementarity indicates support for decision rules based on multiple strategies (e.g., both additive–compensatory and elimination strategies). The experimental results support the validity of the extended effort–accuracy–restrictiveness framework and the effects of strategy normativeness, but not the effects of strategy complementarity. In addition to the perceptions of cognitive effort and advice quality, perceived strategy restrictiveness exerts a significant influence on consumers’ intentions to use online decision aids. The additive–compensatory aid is perceived to be less restrictive, of higher quality, and less effortful than the elimination aid, whereas the hybrid aid is not perceived to be any different from the additive–compensatory aid.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it