Using Recommendation Agents to Cope with Information Overload
Bibliographic record
Abstract
Integrating the traditional and structural approaches to information load measurement, this research investigates the impact of information overload on decision strategy. Decision strategy refers to whether a consumer uses a recommendation agent and the consumer's reactance behavior to the agent advice (whether the chosen product was the same or different from the recommended product). The research further shows the effects of information overload and decision strategy on choice quality, choice confidence, and e-store interactivity. The experiment, which involved 466 consumers, had three levels for the number of alternatives (6, 18, and 30), three levels for the number of attributes (15, 25, and 35), and two different attribute distributions across alternatives (proportional vs. disproportional). The results contribute to the literature of information overload and decision support systems by underscoring that (1) the relationship between information load and perceived overload is curvilinear, (2) information overload augments recommendation agent use and conformance to the recommendation, (3) the positive impact of using a recommendation agent on choice quality increases with information overload, and (4) consumers become more confident in their choices and perceive higher e-store interactivity when they conform to product recommendations. As such, the results help to explain some conflicting findings in the information overload literature and contribute to practice by highlighting the importance of decision aid tools in information-intensive environments.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".