Interactive Decision Aids for Consumer Decision Making in E-Commerce: The Influence of Perceived Strategy Restrictiveness1
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.
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 | 0.007 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".