Assessing the Design Choices for Online Recommendation Agents for Older Adults: Older Does Not Always Mean Simpler Information Technology1
Bibliographic record
Abstract
Grounded in the aging and complexity literature, this experimental study investigated the moderating role of individuals’ cognitive age on the impact of recommendation agent (RA) comprehensiveness (i.e., amount of detail involved in using an RA) on users’ perceptions regarding RA complexity and RA usefulness. An experiment involving 140 online shoppers was conducted to understand the experiences of cognitively younger and older adults while using low or high comprehensiveness RAs designed for this study. Results reveal the tension that exists for older adults when using highly comprehensive RAs, as they perceive them to be more complex but also more useful in providing recommended products. The finding that cognitively older adults perceive high comprehensiveness RAs to be more useful compared to low comprehensiveness RAs provides a novel insight to the information systems literature, as it is contrary to the prevalent belief that “the older the user, the simpler the information technology should be.” Theoretically, this study improves our understanding of how increasing levels of RA comprehensiveness differentially affects the perceptions of RA complexity and RA usefulness of users of different cognitive ages. For practitioners, the results provide important guidelines about the kind of RA that is appropriate for consumers with different cognitive ages.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".