Assessing Screening and Evaluation Decision Support Systems: A Resource-Matching Approach
Bibliographic record
Abstract
This research explores how consumers use online decision aids with screening and evaluation support functionalities under varying product attribute-load conditions. Drawing on resource-matching theory, we conducted a 3 × 2 factorial experiment to test the interaction between decision aid features (i.e., low versus high-screening support, and aids with weight assignment and computation decision tools) and attribute load (i.e., large versus small number of product attributes) on decision performance. The findings reveal that: (1) where the decision aids render cognitive resources that match those demanded for the task environment, consumers will process more information and decision performance will be enhanced; (2) where the decision aids render cognitive resources that exceed those demanded for the task environment, consumers will engage in less task-related elaboration of decision-making issues to the detriment of decision performance; and (3) where the decision aids render cognitive resources that fall short of those demanded for the task environment, consumers will use simplistic heuristic decision strategies to the detriment of decision performance or invest additional effort in information processing to attain a better decision performance if they perceive the additional investments in effort to be manageable.
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 | 0.010 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".