The influence of effort, accuracy, and negative emotions on product choice-strategies: Evaluations of recommendation agents on desktops versus handheld devices
Bibliographic record
Abstract
Intelligent product recommendation agents (RA) are used widely in e-commerce to reduce consumersâ effort and to increase the accuracy of their decisions. This study investigates how to design RAs, for desktop and handheld devices, to alleviate the negative emotions associated with the normative decision-strategy which generates accurate decisions but only with extensive effort on the part of users. Decision-strategies and preference-elicitation methods (i.e., question and answer sessions for RAs to identify the needs of individual consumers) that are employed by RAs generate different levels of effort, accuracy, and the negative emotions, while the additional cognitive effort necessitated when using limited handheld devices moderates such relationship. Provision of the RA that mitigates the negative emotions will instigate the decision-maker to choose the normative decision-strategy for emotion-laden tasks. This study extends RA literature into the area of emotions related to decision-making and into the context of mobile computing.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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".