The Role of Product Recommendation Agents in Collaborative Online Shopping
Bibliographic record
Abstract
Over the last decade, a wealth of research has examined the potential benefits of product recommendation agents (PRAs) for improving outcomes for e-commerce consumers and vendors. To date, however, this research has largely overlooked the fundamentally social nature of shopping. In particular, people often shop collaboratively (together) and for hedonic reasons (for enjoyment), but researchers have focused almost exclusively on isolated individuals using a PRA for utilitarian reasons. This study aims to extend past research by examining the effect of PRAs on both utilitarian and hedonic value in the context of collaborative online shopping (COS). Because communication is an inherent part of any collaborative activity, our model examines both the indirect effect of PRA use on shopping value through its effect on communication among shoppers, and the direct effect of PRA use on shopping value. We propose a moderated mediation model that predicts that: task-oriented communication (TOC) positively affects utilitarian shopping value, and social-emotional communication (SEC) positively affects hedonic shopping value; (2) PRA use reduces the amount of SECs, (3) PRA use reduces the importance of TOC; and (4) PRA use directly increases utilitarian value and directly reduces hedonic value. We describe an experiment where we are planning to test the proposed model, and its intended contributions for theory and practice.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".